Federated learning for medical image analysis: A survey

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easil...

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Published inPattern recognition Vol. 151; p. 110424
Main Authors Guan, Hao, Yap, Pew-Thian, Bozoki, Andrea, Liu, Mingxia
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.07.2024
Subjects
Data privacy
Federated learning
Machine learning
Medical image analysis
Data privacy
Federated learning
Machine learning
Medical image analysis
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Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field. •Summarize existing methods from a system perspective.•Introduce different methods in a “question–answer” paradigm.•Introduce software platforms and benchmark datasets.•Conduct an experimental study.
AbstractList Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field. •Summarize existing methods from a system perspective.•Introduce different methods in a “question–answer” paradigm.•Introduce software platforms and benchmark datasets.•Conduct an experimental study.
Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.
Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.
ArticleNumber 110424
Author Liu, Mingxia
Yap, Pew-Thian
Bozoki, Andrea
Guan, Hao
AuthorAffiliation a Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
b Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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  givenname: Pew-Thian
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  organization: Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Cites_doi 10.1609/aaai.v36i1.19993
10.1007/s10994-019-05855-6
10.1093/nsr/nwx106
10.1002/jmri.21049
10.1109/TMI.2014.2377694
10.1109/TMI.2022.3220757
10.1109/CVPR42600.2020.00975
10.1109/TMI.2022.3222126
10.1109/TMI.2023.3270140
10.1016/j.patcog.2017.10.009
10.1016/j.neuroimage.2023.119863
10.1109/TKDE.2021.3070203
10.1109/TPAMI.2020.2981604
10.1145/3460427
10.1016/j.ejmp.2021.04.016
10.1016/j.media.2019.03.009
10.1145/3447548.3467185
10.1109/ICCV.2017.244
10.1016/j.nic.2005.09.008
10.1109/TMI.2021.3090082
10.1016/j.media.2020.101759
10.3390/s21010167
10.1109/JSEN.2021.3076767
10.1109/TMI.2023.3263072
10.1109/JBHI.2018.2824327
10.1109/CVPR46437.2021.01607
10.1016/j.knosys.2021.106775
10.1145/3412357
10.1109/TII.2021.3138919
10.1109/CVPR.2017.369
10.1109/ACCESS.2021.3111118
10.1109/TMI.2022.3188728
10.1109/CVPR52688.2022.02020
10.1109/JBHI.2022.3185956
10.1145/3501813
10.1109/JBHI.2017.2731873
10.1038/s41746-020-00323-1
10.1038/s41598-018-37257-4
10.1109/ACCESS.2023.3260027
10.1038/s41598-022-05539-7
10.1109/JBHI.2022.3185673
10.1109/TNNLS.2022.3152527
10.1016/j.media.2021.101992
10.1145/3501296
10.1038/s41591-022-02155-w
10.2196/22269
10.1109/JBHI.2020.3040015
10.1109/TMI.2009.2028576
10.1016/j.media.2017.07.005
10.1016/j.asoc.2021.107330
10.1109/34.75512
10.1038/sdata.2018.161
10.1109/TMI.2022.3233574
10.1016/j.media.2013.12.002
10.1109/CVPR46437.2021.00245
10.1109/MSP.2020.2975749
10.1016/j.dib.2019.104863
10.1006/nimg.2001.0978
10.1109/TMI.2023.3235757
10.1038/mp.2013.78
10.1038/s41467-023-40687-y
10.1016/j.media.2022.102680
10.1016/j.media.2021.102305
10.1371/journal.pone.0224365
10.1016/j.media.2021.102298
10.1109/ACCESS.2021.3105929
10.1109/TMI.2022.3202106
10.1038/s41591-021-01506-3
10.1088/1361-6560/ac97d9
10.1038/s41598-022-07186-4
10.1016/j.media.2022.102424
10.1109/TNNLS.2013.2292894
10.1109/ACCESS.2020.3010287
10.1007/s00521-019-04051-w
10.1145/3298981
10.1561/2200000083
10.1109/TMI.2023.3239391
10.3390/data5040089
10.1109/TPAMI.2018.2858821
10.1109/TMI.2022.3225083
10.1109/RBME.2017.2651164
10.1109/TMI.2022.3233405
10.1016/j.media.2020.101765
10.1109/JIOT.2021.3056185
10.1109/TMI.2014.2303821
10.1038/s42256-021-00337-8
10.1007/s00521-020-05596-x
10.1109/TBME.2021.3117407
10.1109/TMI.2018.2837502
10.1038/ng.2764
10.1200/CCI.20.00060
10.1038/sdata.2016.3
10.1371/journal.pone.0255809
10.1109/TMI.2023.3234450
10.1109/ACCESS.2023.3238823
10.1109/CVPR46437.2021.00107
10.1109/JBHI.2022.3198440
10.1109/TMI.2022.3192483
10.1145/3570953
10.1609/aaai.v33i01.3301590
10.1109/TMI.2022.3220706
10.1016/j.media.2021.102136
10.1038/nn.4393
10.1109/JBHI.2023.3274498
10.1038/nature14539
10.1109/TMI.2021.3075856
10.1016/j.dib.2020.106221
10.1109/CVPR42600.2020.00674
10.1109/OJCS.2022.3206407
10.1016/j.neuroimage.2016.05.053
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References Alkhunaizi, Kamzolov, Takáč, Nandakumar (b32) 2022
Q. Yang, J. Zhang, W. Hao, G.P. Spell, L. Carin, Flop: Federated learning on medical datasets using partial networks, in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 3845–3853.
K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729–9738.
T Dinh, Tran, Nguyen (b40) 2020; 33
Li, Gu, Dvornek, Staib, Ventola, Duncan (b48) 2020; 65
Liu, Xu, Peng, Xiong (b83) 2018; 31
Ronneberger, Fischer, Brox (b37) 2015
Muckley, Riemenschneider, Radmanesh, Kim, Jeong, Ko, Jun, Shin, Hwang, Mostapha (b118) 2021; 40
Kairouz, McMahan, Avent, Bellet, Bennis, Bhagoji, Bonawitz, Charles, Cormode, Cummings (b14) 2021; 14
Dong, Kampffmeyer, Voiculescu (b127) 2022
Ke, Shen, Lu (b46) 2021
Spanhol, Oliveira, Petitjean, Heutte (b133) 2016
Sheller, Reina, Edwards, Martin, Bakas (b126) 2019
Peng, Wang, Dvornek, Zhu, Li (b81) 2023; 42
Yan, Wicaksana, Wang, Yang, Cheng (b53) 2020; 25
Zhu, Luo (b78) 2022
Liu, Li, Yuan (b154) 2022
Li, Wen, Wu, Hu, Wang, Li, Liu, He (b15) 2021
J.-Y. Zhu, T. Park, P. Isola, A.A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2223–2232.
Deng, Dong, Socher, Li, Li, Fei-Fei (b6) 2009
Li, Milletarì, Xu, Rieke, Hancox, Zhu, Baust, Cheng, Ourselin, Cardoso (b90) 2019
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R.M. Summers, Chestx-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2097–2106.
Roth (b156) 2021
Cassidy, Kendrick, Brodzicki, Jaworek-Korjakowska, Yap (b114) 2022; 75
Hinton, Vinyals, Dean (b75) 2015
Chaitanya, Erdil, Karani, Konukoglu (b56) 2020; 33
Luo, Wu (b86) 2022
Miller (b106) 2016; 19
Yang, Liu, Chen, Tong (b17) 2019; 10
I. Misra, L.v.d. Maaten, Self-supervised learning of pretext-invariant representations, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 6707–6717.
Bernard (b111) 2018; 37
General Data Protection Regulation (b11) 2019
Zhang, Zhou, Lu, Wang, Zhu, Sun, Wang, Lo, Wang (b31) 2021; 8
A. Xu, W. Li, P. Guo, D. Yang, H.R. Roth, A. Hatamizadeh, C. Zhao, D. Xu, H. Huang, Z. Xu, Closing the generalization gap of cross-silo federated medical image segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20866–20875.
J. Irvin, et al., Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 590–597.
H. Yin, A. Mallya, A. Vahdat, J.M. Alvarez, J. Kautz, P. Molchanov, See through gradients: Image batch recovery via gradinversion, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 16337–16346.
Wicaksana, Yan, Yang, Liu, Fan, Cheng (b39) 2022; 26
Wang, Lan, Liu, Ouyang, Qin, Lu, Chen, Zeng, Yu (b174) 2023; 35
Hosseini, Sikaroudi, Babaie, Tizhoosh (b84) 2023; 42
Adnan, Kalra, Cresswell, Taylor, Tizhoosh (b159) 2022; 12
Guan, Liu (b38) 2023; 268
Zhou, Liu, Qiao, Xiang, Loy (b173) 2023; 45
M. Malekzadeh, B. Hasircioglu, N. Mital, K. Katarya, M.E. Ozfatura, D. Gündüz, Dopamine: Differentially private federated learning on medical data, in: The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence, PPAI-21, 2021, pp. 1–9.
Wang, Lin, Wong (b110) 2020; 10
Chiruvella, Guddati (b28) 2021; 10
Gürler, Rekik (b120) 2023; 42
Noman, Rahaman, Pranto, Rahman (b183) 2023
Chakravarty, Kar, Sethuraman, Sheet (b43) 2021
M. Jiang, Z. Wang, Q. Dou, Harmofl: Harmonizing local and global drifts in federated learning on heterogeneous medical images, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36, 2022, pp. 1087–1095.
Carbonneau, Cheplygina, Granger, Gagnon (b69) 2018; 77
Karimi, Dou, Warfield, Gholipour (b179) 2020; 65
Huang, Hu, Liu, Xue, Zhu, Song, Tan (b64) 2022
He (b77) 2023
Guan, Liu (b3) 2022; 69
Miyato, Maeda, Koyama, Ishii (b79) 2018; 41
T. Li, A.K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, V. Smith, Federated optimization in heterogeneous networks, in: Proceedings of Machine Learning and Systems, Vol. 2, 2020, pp. 429–450.
Lyu, Yu, Ma, Chen, Sun, Zhao, Yang, Philip (b169) 2022
Di Martino (b103) 2014; 19
Qin, Yang, Gao, Hu, Shen (b178) 2022
LeCun, Bengio, Hinton (b5) 2015; 521
Zhang, Xie, Bai, Yu, Li, Gao (b19) 2021; 216
Rieke, Hancox, Li, Milletari, Roth, Albarqouni, Bakas, Galtier, Landman, Maier-Hein (b25) 2020; 3
Wagner, Fuchs, Tolkach, Mukhopadhyay (b47) 2022
Silva, Altmann, Gutman, Lorenzi (b98) 2020
Yang, Shi, Ni (b119) 2021
Chowdhury, Rahman, Khandakar, Mazhar, Kadir, Mahbub, Islam, Khan, Iqbal, Al Emadi (b109) 2020; 8
Dinsdale, Jenkinson, Namburete (b49) 2022
Rahman, Ahmed, Akhter, Hasan, Amin, Aziz, Islam, Mukta, Islam (b18) 2021; 9
Litjens, Debats, Barentsz, Karssemeijer, Huisman (b153) 2014; 33
Fumero, Alayón, Sanchez, Sigut, Gonzalez-Hernandez (b146) 2011
Hatamizadeh, Yin, Molchanov, Myronenko, Li, Dogra, Feng, Flores, Kautz, Xu (b94) 2023
Cancer Genome Atlas Research Network, Weinstein, Collisson, Mills, Shaw, Ozenberger, Ellrott, Shmulevich, Sande, Stuart (b116) 2013; 45
Li, Xu, Cao, Liu, Zhang, Chen, Dai (b34) 2022
Feki, Ammar, Kessentini, Muhammad (b128) 2021; 106
Tan, Yu, Cui, Yang (b171) 2022
Nguyen, Pham, Pathirana, Ding, Seneviratne, Lin, Dobre, Hwang (b23) 2022; 55
Frénay, Verleysen (b71) 2013; 25
Litjens, Toth, Van De Ven, Hoeks, Kerkstra, van Ginneken, Vincent, Guillard, Birbeck, Zhang (b115) 2014; 18
Zhu, Liu, Han (b89) 2019; 32
Foley, Sheller, Edwards, Pati, Riviera, Sharma, Moorthy, Wang, Martin, Mirhaji (b97) 2022; 67
Menze, Jakab, Bauer, Kalpathy-Cramer, Farahani, Kirby, Burren, Porz, Slotboom, Wiest (b104) 2014; 34
Yang, Xu, Li, Myronenko, Roth, Harmon, Xu, Turkbey, Turkbey, Wang (b72) 2021; 70
Qiu, Cheng, Gao, Xiong, Ren (b145) 2023
Yap, Pons, Marti, Ganau, Sentis, Zwiggelaar, Davison, Marti (b137) 2017; 22
Wu, Zeng, Wang, Shi, Hu (b59) 2022; 81
Budrionis, Miara, Miara, Wilk, Bellika (b96) 2021; 9
van Ravesteijn, van Wijk, Vos, Truyen, Peters, Stoker, van Vliet (b166) 2009; 29
Yan, Qu, Wei, Huang, Shen, Rubin, Xing, Zhou (b33) 2023
Chen, Yang, Zhu, Peng, Yuan (b82) 2022; 41
Sohan, Basalamah (b27) 2023; 11
Aouedi, Sacco, Piamrat, Marchetto (b26) 2023; 27
Yang, Song, King, Xu (b66) 2022
Bulten (b160) 2019; 9
McMahan, Moore, Ramage, Hampson, y Arcas (b12) 2017
Kawahara, Daneshvar, Argenziano, Hamarneh (b140) 2018; 23
Lu, Chen, Kong, Lipkova, Singh, Williamson, Chen, Mahmood (b73) 2022; 76
Song, Kim, Park, Shin, Lee (b70) 2023; 34
X. Li, K. Huang, W. Yang, S. Wang, Z. Zhang, On the Convergence of FedAvg on Non-IID Data, in: Proceedings of International Conference on Learning Representations, 2020, pp. 1–12.
Tschandl, Rosendahl, Kittler (b113) 2018; 5
Zhou (b65) 2018; 5
Wicaksana, Yan, Zhang, Huang, Wu, Yang, Cheng (b134) 2023
Geiping, Bauermeister, Dröge, Moeller (b87) 2020; 33
Andreux, du Terrail, Beguier, Tramel (b50) 2020
Li, Kar, Ravikumar, Frangi, Fidler (b131) 2020
Codella (b143) 2018
NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures (b150) 2021
Lalande (b130) 2020; 5
Vabalas, Gowen, Poliakoff, Casson (b9) 2019; 14
Li (b155) 2021; 16
US Department of Health and Human Services (b10) 2020
Mueller, Weiner, Thal, Petersen, Jack, Jagust, Trojanowski, Toga, Beckett (b101) 2005; 15
Codella, Rotemberg, Tschandl, Celebi, Dusza, Gutman, Helba, Kalloo, Liopyris, Marchetti (b139) 2019
Raudys, Jain (b8) 1991; 13
Feng, Yan, Wang, Xu, Shao, Fu (b41) 2023; 42
Pfitzner, Steckhan, Arnrich (b24) 2021; 21
LaMontagne, Benzinger, Morris, Keefe, Hornbeck, Xiong, Grant, Hassenstab, Moulder, Vlassenko (b121) 2019
Pacheco, Lima, Salomão, Krohling, Biral, de Angelo, Alves, Esgario, Simora, Castro (b141) 2020; 32
Islam, Reza, Kaosar, Parvez (b122) 2022
Qayyum, Ahmad, Ahsan, Al-Fuqaha, Qadir (b172) 2022; 3
Zhang, Yang (b62) 2021; 34
Campello (b112) 2021; 40
Geng, Huang, Chen (b177) 2020; 43
Jiang, Yang, Cheng, Dou (b45) 2023; 42
Van Engelen, Hoos (b67) 2020; 109
Quellec, Cazuguel, Cochener, Lamard (b68) 2017; 10
Fan, Su, Gao, Hu, Zeng (b85) 2021
Qi, Yang, He, Liu, Islam, Li (b124) 2022
He, Carass, Zuo, Dewey, Prince (b175) 2021; 72
Ziller, Trask, Lopardo, Szymkow, Wagner, Bluemke, Nounahon, Passerat-Palmbach, Prakash, Rose (b95) 2021
Vellido (b186) 2020; 32
Wu, Zeng, Wang, Shi, Hu (b60) 2021
Xu, Deng, Gateno, Yan (b151) 2023
Li, Liu, Hu, Tuan, Yu (b187) 2023
Kaissis, Ziller, Passerat-Palmbach, Ryffel, Usynin, Trask, Lima, Mancuso, Jungmann, Steinborn (b93) 2021; 3
Litjens, Kooi, Bejnordi, Setio, Ciompi, Ghafoorian, Van Der Laak, Van Ginneken, Sánchez (b4) 2017; 42
Kassem, Alapatt, Mascagni, AI4SafeChole, Karargyris, Padoy (b74) 2023; 42
Huang, Huang, Li, Li (b180) 2023; 42
Antunes, André da Costa, Küderle, Yari, Eskofier (b21) 2022; 13
Chang, Yan, Zhou, Qu, He, Zhang, Baskaran, Al’Aref, Li, Zhang (b80) 2023; 14
Zhu, Chen, Yuan (b149) 2023
Wang, Jin, Stoyanov, Wang (b147) 2023
Bzdok, Eickenberg, Grisel, Thirion, Varoquaux (b164) 2015; 28
Dwork, Roth (b91) 2014; 9
Elmas, Dar, Korkmaz, Ceyani, Susam, Ozbey, Avestimehr, Cukur (b161) 2023; 42
Simpson (b157) 2019
Cheplygina, de Bruijne, Pluim (b2) 2019; 54
Smith, Chiang, Sanjabi, Talwalkar (b63) 2017; 30
Knoll, Zbontar, Sriram, Muckley, Bruno, Defazio, Parente, Geras, Katsnelson, Chandarana (b117) 2020; 2
Chen, Zhu, Yang, Yuan (b142) 2021
Dayan, Roth, Zhong, Harouni, Gentili, Abidin, Liu, Costa, Wood, Tsai (b100) 2021; 27
Divya, Shantha Selva Kumari (b163) 2021; 33
Kumar, Purohit, Bharti, Singh, Singh (b76) 2021; 18
Bdair, Navab, Albarqouni (b138) 2021
Dugas, Jorge, Cukierski (b144) 2015
Zhu, Cao, Saxena, Jiang, Ferradi (b181) 2023; 55
Satariano (b30) 2019; 21
California Consumer Privacy Act (CCPA) (b29) 2018
Wachinger (b165) 2016; 139
Barragán-Montero, J
10.1016/j.patcog.2024.110424_b44
Smith (10.1016/j.patcog.2024.110424_b63) 2017; 30
Dong (10.1016/j.patcog.2024.110424_b61) 2021
Luo (10.1016/j.patcog.2024.110424_b86) 2022
Liu (10.1016/j.patcog.2024.110424_b154) 2022
Wang (10.1016/j.patcog.2024.110424_b147) 2023
Wachinger (10.1016/j.patcog.2024.110424_b165) 2016; 139
Kassem (10.1016/j.patcog.2024.110424_b74) 2023; 42
Menze (10.1016/j.patcog.2024.110424_b104) 2014; 34
LeCun (10.1016/j.patcog.2024.110424_b5) 2015; 521
Zhang (10.1016/j.patcog.2024.110424_b136) 2022; Vol. 10
Zhang (10.1016/j.patcog.2024.110424_b19) 2021; 216
Chiruvella (10.1016/j.patcog.2024.110424_b28) 2021; 10
He (10.1016/j.patcog.2024.110424_b77) 2023
Zhu (10.1016/j.patcog.2024.110424_b181) 2023; 55
Rieke (10.1016/j.patcog.2024.110424_b25) 2020; 3
Jiang (10.1016/j.patcog.2024.110424_b45) 2023; 42
Barragán-Montero (10.1016/j.patcog.2024.110424_b1) 2021; 83
10.1016/j.patcog.2024.110424_b51
Peng (10.1016/j.patcog.2024.110424_b81) 2023; 42
Chowdhury (10.1016/j.patcog.2024.110424_b109) 2020; 8
Litjens (10.1016/j.patcog.2024.110424_b153) 2014; 33
Li (10.1016/j.patcog.2024.110424_b187) 2023
10.1016/j.patcog.2024.110424_b35
Lu (10.1016/j.patcog.2024.110424_b73) 2022; 76
Pacheco (10.1016/j.patcog.2024.110424_b141) 2020; 32
10.1016/j.patcog.2024.110424_b168
Satariano (10.1016/j.patcog.2024.110424_b30) 2019; 21
Di Martino (10.1016/j.patcog.2024.110424_b103) 2014; 19
10.1016/j.patcog.2024.110424_b167
Budrionis (10.1016/j.patcog.2024.110424_b96) 2021; 9
Roth (10.1016/j.patcog.2024.110424_b185) 2020
Yin (10.1016/j.patcog.2024.110424_b20) 2021; 54
Dwork (10.1016/j.patcog.2024.110424_b91) 2014; 9
Miyato (10.1016/j.patcog.2024.110424_b79) 2018; 41
Kairouz (10.1016/j.patcog.2024.110424_b14) 2021; 14
Divya (10.1016/j.patcog.2024.110424_b163) 2021; 33
Vellido (10.1016/j.patcog.2024.110424_b186) 2020; 32
Kawahara (10.1016/j.patcog.2024.110424_b140) 2018; 23
Lalande (10.1016/j.patcog.2024.110424_b130) 2020; 5
Cheplygina (10.1016/j.patcog.2024.110424_b2) 2019; 54
Li (10.1016/j.patcog.2024.110424_b16) 2020; 37
Ziller (10.1016/j.patcog.2024.110424_b95) 2021
Wang (10.1016/j.patcog.2024.110424_b110) 2020; 10
Pernet (10.1016/j.patcog.2024.110424_b123) 2016; 3
Tzourio-Mazoyer (10.1016/j.patcog.2024.110424_b162) 2002; 15
Li (10.1016/j.patcog.2024.110424_b131) 2020
Zhang (10.1016/j.patcog.2024.110424_b42) 2022; 26
Aouedi (10.1016/j.patcog.2024.110424_b26) 2023; 27
Agbley (10.1016/j.patcog.2024.110424_b132) 2023
Song (10.1016/j.patcog.2024.110424_b70) 2023; 34
Kumar (10.1016/j.patcog.2024.110424_b182) 2021; 21
US Department of Health and Human Services (10.1016/j.patcog.2024.110424_b10) 2020
Li (10.1016/j.patcog.2024.110424_b90) 2019
Linardos (10.1016/j.patcog.2024.110424_b129) 2022; 12
Wang (10.1016/j.patcog.2024.110424_b174) 2023; 35
Chang (10.1016/j.patcog.2024.110424_b80) 2023; 14
Ke (10.1016/j.patcog.2024.110424_b46) 2021
Li (10.1016/j.patcog.2024.110424_b48) 2020; 65
10.1016/j.patcog.2024.110424_b13
Zhou (10.1016/j.patcog.2024.110424_b65) 2018; 5
10.1016/j.patcog.2024.110424_b148
General Data Protection Regulation (10.1016/j.patcog.2024.110424_b11) 2019
Cassidy (10.1016/j.patcog.2024.110424_b114) 2022; 75
Bilic (10.1016/j.patcog.2024.110424_b152) 2023; 84
Dong (10.1016/j.patcog.2024.110424_b127) 2022
Li (10.1016/j.patcog.2024.110424_b34) 2022
Yang (10.1016/j.patcog.2024.110424_b66) 2022
Knoll (10.1016/j.patcog.2024.110424_b117) 2020; 2
Xia (10.1016/j.patcog.2024.110424_b170) 2023; 11
Litjens (10.1016/j.patcog.2024.110424_b115) 2014; 18
Zhu (10.1016/j.patcog.2024.110424_b89) 2019; 32
Islam (10.1016/j.patcog.2024.110424_b122) 2022
Guan (10.1016/j.patcog.2024.110424_b3) 2022; 69
Hinton (10.1016/j.patcog.2024.110424_b75) 2015
Van Engelen (10.1016/j.patcog.2024.110424_b67) 2020; 109
LaMontagne (10.1016/j.patcog.2024.110424_b121) 2019
10.1016/j.patcog.2024.110424_b88
Yang (10.1016/j.patcog.2024.110424_b119) 2021
Wu (10.1016/j.patcog.2024.110424_b60) 2021
Zhu (10.1016/j.patcog.2024.110424_b78) 2022
Feki (10.1016/j.patcog.2024.110424_b128) 2021; 106
Zhou (10.1016/j.patcog.2024.110424_b173) 2023; 45
Raudys (10.1016/j.patcog.2024.110424_b8) 1991; 13
Ronneberger (10.1016/j.patcog.2024.110424_b37) 2015
Roy (10.1016/j.patcog.2024.110424_b184) 2019
Campello (10.1016/j.patcog.2024.110424_b112) 2021; 40
Geiping (10.1016/j.patcog.2024.110424_b87) 2020; 33
Yan (10.1016/j.patcog.2024.110424_b53) 2020; 25
Dugas (10.1016/j.patcog.2024.110424_b144) 2015
Zhu (10.1016/j.patcog.2024.110424_b149) 2023
Chen (10.1016/j.patcog.2024.110424_b142) 2021
Wicaksana (10.1016/j.patcog.2024.110424_b39) 2022; 26
Flanders (10.1016/j.patcog.2024.110424_b105) 2020; 2
Spanhol (10.1016/j.patcog.2024.110424_b133) 2016
Hatamizadeh (10.1016/j.patcog.2024.110424_b94) 2023
Foley (10.1016/j.patcog.2024.110424_b97) 2022; 67
10.1016/j.patcog.2024.110424_b92
Bernard (10.1016/j.patcog.2024.110424_b111) 2018; 37
Adnan (10.1016/j.patcog.2024.110424_b159) 2022; 12
Kaissis (10.1016/j.patcog.2024.110424_b93) 2021; 3
McMahan (10.1016/j.patcog.2024.110424_b12) 2017
Liu (10.1016/j.patcog.2024.110424_b83) 2018; 31
Pfitzner (10.1016/j.patcog.2024.110424_b24) 2021; 21
Rajendran (10.1016/j.patcog.2024.110424_b22) 2021; 5
Liu (10.1016/j.patcog.2024.110424_b125) 2021
Lin (10.1016/j.patcog.2024.110424_b7) 2014
Qiu (10.1016/j.patcog.2024.110424_b145) 2023
Qi (10.1016/j.patcog.2024.110424_b124) 2022
Elmas (10.1016/j.patcog.2024.110424_b161) 2023; 42
Hosseini (10.1016/j.patcog.2024.110424_b84) 2023; 42
Simpson (10.1016/j.patcog.2024.110424_b157) 2019
Deng (10.1016/j.patcog.2024.110424_b6) 2009
Sohan (10.1016/j.patcog.2024.110424_b27) 2023; 11
He (10.1016/j.patcog.2024.110424_b175) 2021; 72
T Dinh (10.1016/j.patcog.2024.110424_b40) 2020; 33
Mueller (10.1016/j.patcog.2024.110424_b101) 2005; 15
Codella (10.1016/j.patcog.2024.110424_b139) 2019
Frénay (10.1016/j.patcog.2024.110424_b71) 2013; 25
Nguyen (10.1016/j.patcog.2024.110424_b23) 2022; 55
Feng (10.1016/j.patcog.2024.110424_b41) 2023; 42
Varsavsky (10.1016/j.patcog.2024.110424_b176) 2020
Xu (10.1016/j.patcog.2024.110424_b151) 2023
Li (10.1016/j.patcog.2024.110424_b155) 2021; 16
California Consumer Privacy Act (CCPA) (10.1016/j.patcog.2024.110424_b29) 2018
Yan (10.1016/j.patcog.2024.110424_b33) 2023
Yang (10.1016/j.patcog.2024.110424_b17) 2019; 10
Alkhunaizi (10.1016/j.patcog.2024.110424_b32) 2022
Sheller (10.1016/j.patcog.2024.110424_b126) 2019
Qin (10.1016/j.patcog.2024.110424_b178) 2022
Gürler (10.1016/j.patcog.2024.110424_b120) 2023; 42
Karimi (10.1016/j.patcog.2024.110424_b179) 2020; 65
Silva (10.1016/j.patcog.2024.110424_b98) 2020
Stripelis (10.1016/j.patcog.2024.110424_b36) 2021
Chakravarty (10.1016/j.patcog.2024.110424_b43) 2021
Lyu (10.1016/j.patcog.2024.110424_b169) 2022
Miller (10.1016/j.patcog.2024.110424_b106) 2016; 19
Roth (10.1016/j.patcog.2024.110424_b156) 2021
Li (10.1016/j.patcog.2024.110424_b15) 2021
Cancer Genome Atlas Research Network (10.1016/j.patcog.2024.110424_b116) 2013; 45
du Terrail (10.1016/j.patcog.2024.110424_b158) 2023; 29
Wicaksana (10.1016/j.patcog.2024.110424_b134) 2023
Codella (10.1016/j.patcog.2024.110424_b143) 2018
Jack (10.1016/j.patcog.2024.110424_b102) 2008; 27
Wagner (10.1016/j.patcog.2024.110424_b47) 2022
Huang (10.1016/j.patcog.2024.110424_b180) 2023; 42
Fumero (10.1016/j.patcog.2024.110424_b146) 2011
Bulten (10.1016/j.patcog.2024.110424_b160) 2019; 9
Qu (10.1016/j.patcog.2024.110424_b52) 2022; 78
van Ravesteijn (10.1016/j.patcog.2024.110424_b166) 2009; 29
Zhang (10.1016/j.patcog.2024.110424_b62) 2021; 34
Dinsdale (10.1016/j.patcog.2024.110424_b49) 2022
Fan (10.1016/j.patcog.2024.110424_b85) 2021
Kholod (10.1016/j.patcog.2024.110424_b99) 2020; 21
Quellec (10.1016/j.patcog.2024.110424_b68) 2017; 10
10.1016/j.patcog.2024.110424_b55
Al-Dhabyani (10.1016/j.patcog.2024.110424_b135) 2020; 28
Guan (10.1016/j.patcog.2024.110424_b38) 2023; 268
10.1016/j.patcog.2024.110424_b54
10.1016/j.patcog.2024.110424_b107
Vabalas (10.1016/j.patcog.2024.110424_b9) 2019; 14
10.1016/j.patcog.2024.110424_b57
Kumar (10.1016/j.patcog.2024.110424_b76) 2021; 18
Muckley (10.1016/j.patcog.2024.110424_b118) 2021; 40
Zhang (10.1016/j.patcog.2024.110424_b31) 2021; 8
10.1016/j.patcog.2024.110424_b58
Dayan (10.1016/j.patcog.2024.110424_b100) 2021; 27
NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures (10.1016/j.patcog.2024.110424_b150) 2021
Rahman (10.1016/j.patcog.2024.110424_b18) 2021; 9
Chen (10.1016/j.patcog.2024.110424_b82) 2022; 41
Antunes (10.1016/j.patcog.2024.110424_b21) 2022; 13
Noman (10.1016/j.patcog.2024.110424_b183) 2023
10.1016/j.patcog.2024.110424_b108
Carbonneau (10.1016/j.patcog.2024.110424_b69) 2018; 77
Yap (10.1016/j.patcog.2024.110424_b137) 2017; 22
Bdair (10.1016/j.patcog.2024.110424_b138) 2021
Tan (10.1016/j.patcog.2024.110424_b171) 2022
Chaitanya (10.1016/j.patcog.2024.110424_b56) 2020; 33
Yang (10.1016/j.patcog.2024.110424_b72) 2021; 70
Tschandl (10.1016/j.patcog.2024.110424_b113) 2018; 5
Litjens (10.1016/j.patcog.2024.110424_b4) 2017; 42
Qayyum (10.1016/j.patcog.2024.110424_b172) 2022; 3
Bzdok (10.1016/j.patcog.2024.110424_b164) 2015; 28
Andreux (10.1016/j.patcog.2024.110424_b50) 2020
Huang (10.1016/j.patcog.2024.110424_b64) 2022
Wu (10.1016/j.patcog.2024.110424_b59) 2022; 81
Geng (10.1016/j.patcog.2024.110424_b177) 2020; 43
References_xml – volume: 3
  start-page: 473
  year: 2021
  end-page: 484
  ident: b93
  article-title: End-to-end privacy preserving deep learning on multi-institutional medical imaging
  publication-title: Nat. Mach. Intell.
– year: 2022
  ident: b34
  article-title: Integrated CNN and federated learning for COVID-19 detection on chest X-ray images
  publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform.
– reference: I. Misra, L.v.d. Maaten, Self-supervised learning of pretext-invariant representations, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 6707–6717.
– volume: 34
  start-page: 5586
  year: 2021
  end-page: 5609
  ident: b62
  article-title: A survey on multi-task learning
  publication-title: IEEE Trans. Knowl. Data Eng.
– volume: 25
  start-page: 2615
  year: 2020
  end-page: 2628
  ident: b53
  article-title: Variation-aware federated learning with multi-source decentralized medical image data
  publication-title: IEEE J. Biomed. Health Inf.
– reference: X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R.M. Summers, Chestx-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2097–2106.
– reference: H. Yin, A. Mallya, A. Vahdat, J.M. Alvarez, J. Kautz, P. Molchanov, See through gradients: Image batch recovery via gradinversion, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 16337–16346.
– volume: 40
  start-page: 3543
  year: 2021
  end-page: 3554
  ident: b112
  article-title: Multi-centre, multi-vendor and multi-disease cardiac segmentation: The M&Ms challenge
  publication-title: IEEE Trans. Med. Imaging
– volume: 28
  start-page: 1
  year: 2020
  end-page: 5
  ident: b135
  article-title: Dataset of breast ultrasound images
  publication-title: Data Brief
– start-page: 1
  year: 2023
  end-page: 16
  ident: b183
  article-title: Blockchain for medical collaboration: A federated learning-based approach for multi-class respiratory disease classification
  publication-title: Healthc. Anal.
– volume: 76
  start-page: 1
  year: 2022
  end-page: 13
  ident: b73
  article-title: Federated learning for computational pathology on gigapixel whole slide images
  publication-title: Med. Image Anal.
– volume: 32
  year: 2019
  ident: b89
  article-title: Deep leakage from gradients
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 33
  start-page: 1083
  year: 2014
  end-page: 1092
  ident: b153
  article-title: Computer-aided detection of prostate cancer in MRI
  publication-title: IEEE Trans. Med. Imaging
– volume: 19
  start-page: 1523
  year: 2016
  end-page: 1536
  ident: b106
  article-title: Multimodal population brain imaging in the UK Biobank prospective epidemiological study
  publication-title: Nature Neurosci.
– volume: 3
  start-page: 1
  year: 2016
  end-page: 6
  ident: b123
  article-title: A structural and functional magnetic resonance imaging dataset of brain tumour patients
  publication-title: Sci. Data
– year: 2023
  ident: b94
  article-title: Do gradient inversion attacks make federated learning unsafe?
  publication-title: IEEE Trans. Med. Imaging
– volume: 5
  start-page: 89
  year: 2020
  ident: b130
  article-title: Emidec: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac MRI
  publication-title: Data
– volume: 18
  start-page: 5648
  year: 2021
  end-page: 5657
  ident: b76
  article-title: Medisecfed: private and secure medical image classification in the presence of malicious clients
  publication-title: IEEE Trans. Ind. Inform.
– reference: M. Malekzadeh, B. Hasircioglu, N. Mital, K. Katarya, M.E. Ozfatura, D. Gündüz, Dopamine: Differentially private federated learning on medical data, in: The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence, PPAI-21, 2021, pp. 1–9.
– volume: 29
  start-page: 120
  year: 2009
  end-page: 131
  ident: b166
  article-title: Computer-aided detection of polyps in CT colonography using logistic regression
  publication-title: IEEE Trans. Med. Imaging
– start-page: 347
  year: 2021
  end-page: 356
  ident: b142
  article-title: Personalized retrogress-resilient framework for real-world medical federated learning
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 84
  start-page: 1
  year: 2023
  end-page: 24
  ident: b152
  article-title: The liver tumor segmentation benchmark (lits)
  publication-title: Med. Image Anal.
– start-page: 67
  year: 2022
  end-page: 76
  ident: b127
  article-title: Learning underrepresented classes from decentralized partially labeled medical images
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 22
  start-page: 1218
  year: 2017
  end-page: 1226
  ident: b137
  article-title: Automated breast ultrasound lesions detection using convolutional neural networks
  publication-title: IEEE J. Biomed. Health Inform.
– volume: 33
  start-page: 8435
  year: 2021
  end-page: 8444
  ident: b163
  article-title: Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification
  publication-title: Neural Comput. Appl.
– volume: 106
  year: 2021
  ident: b128
  article-title: Federated learning for COVID-19 screening from Chest X-ray images
  publication-title: Appl. Soft Comput.
– volume: 42
  year: 2023
  ident: b161
  article-title: Federated learning of generative image priors for MRI reconstruction
  publication-title: IEEE Trans. Med. Imaging
– volume: 9
  start-page: 1
  year: 2019
  end-page: 10
  ident: b160
  article-title: Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
  publication-title: Sci. Rep.
– volume: 41
  start-page: 3663
  year: 2022
  end-page: 3674
  ident: b82
  article-title: Personalized retrogress-resilient federated learning toward imbalanced medical data
  publication-title: IEEE Trans. Med. Imaging
– volume: 14
  start-page: 1
  year: 2021
  end-page: 210
  ident: b14
  article-title: Advances and open problems in federated learning
  publication-title: Found. Trends® Mach. Learn.
– volume: 28
  year: 2015
  ident: b164
  article-title: Semi-supervised factored logistic regression for high-dimensional neuroimaging data
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 83
  start-page: 242
  year: 2021
  end-page: 256
  ident: b1
  article-title: Artificial intelligence and machine learning for medical imaging: A technology review
  publication-title: Phys. Medica
– volume: 54
  start-page: 1
  year: 2021
  end-page: 36
  ident: b20
  article-title: A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
  publication-title: ACM Comput. Surv.
– volume: 18
  start-page: 359
  year: 2014
  end-page: 373
  ident: b115
  article-title: Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge
  publication-title: Med. Image Anal.
– volume: 78
  year: 2022
  ident: b52
  article-title: Handling data heterogeneity with generative replay in collaborative learning for medical imaging
  publication-title: Med. Image Anal.
– start-page: 181
  year: 2020
  end-page: 191
  ident: b185
  article-title: Federated learning for breast density classification: A real-world implementation
  publication-title: MICCAI Workshop on Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning
– volume: 5
  start-page: 1
  year: 2021
  end-page: 11
  ident: b22
  article-title: Cloud-based federated learning implementation across medical centers
  publication-title: JCO Clin. Cancer Inform.
– start-page: 428
  year: 2020
  end-page: 436
  ident: b176
  article-title: Test-time unsupervised domain adaptation
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru, October 4–8, 2020
– volume: 139
  start-page: 470
  year: 2016
  end-page: 479
  ident: b165
  article-title: Domain adaptation for Alzheimer’s disease diagnostics
  publication-title: NeuroImage
– year: 2019
  ident: b184
  article-title: Braintorrent: A peer-to-peer environment for decentralized federated learning
– volume: 37
  start-page: 50
  year: 2020
  end-page: 60
  ident: b16
  article-title: Federated learning: Challenges, methods, and future directions
  publication-title: IEEE Signal Process. Mag.
– year: 2022
  ident: b66
  article-title: A survey on deep semi-supervised learning
  publication-title: IEEE Trans. Knowl. Data Eng.
– volume: 16
  start-page: 1
  year: 2021
  end-page: 26
  ident: b155
  article-title: Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations
  publication-title: PLOS ONE
– volume: 29
  start-page: 135
  year: 2023
  end-page: 146
  ident: b158
  article-title: Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer
  publication-title: Nature Med.
– volume: 12
  start-page: 1953
  year: 2022
  ident: b159
  article-title: Federated learning and differential privacy for medical image analysis
  publication-title: Sci. Rep.
– year: 2023
  ident: b134
  article-title: FedMix: Mixed supervised federated learning for medical image segmentation
  publication-title: IEEE Trans. Med. Imaging
– start-page: 1
  year: 2022
  end-page: 5
  ident: b86
  article-title: Fedsld: Federated learning with shared label distribution for medical image classification
  publication-title: 2022 IEEE 19th International Symposium on Biomedical Imaging
– volume: 33
  start-page: 16937
  year: 2020
  end-page: 16947
  ident: b87
  article-title: Inverting gradients-how easy is it to break privacy in federated learning?
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 41
  start-page: 1979
  year: 2018
  end-page: 1993
  ident: b79
  article-title: Virtual adversarial training: a regularization method for supervised and semi-supervised learning
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– volume: 521
  start-page: 436
  year: 2015
  end-page: 444
  ident: b5
  article-title: Deep learning
  publication-title: Nature
– volume: 25
  start-page: 845
  year: 2013
  end-page: 869
  ident: b71
  article-title: Classification in the presence of label noise: A survey
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– reference: Q. Liu, C. Chen, J. Qin, Q. Dou, P.-A. Heng, Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 1013–1023.
– volume: 12
  start-page: 3551
  year: 2022
  ident: b129
  article-title: Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease
  publication-title: Sci. Rep.
– volume: 26
  start-page: 4635
  year: 2022
  end-page: 4644
  ident: b42
  article-title: SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging
  publication-title: IEEE J. Biomed. Health Inf.
– volume: 3
  start-page: 172
  year: 2022
  end-page: 184
  ident: b172
  article-title: Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge
  publication-title: IEEE Open J. Comput. Soc.
– volume: 42
  year: 2023
  ident: b84
  article-title: Proportionally fair hospital collaborations in federated learning of histopathology images
  publication-title: IEEE Trans. Med. Imaging
– year: 2021
  ident: b150
– volume: 13
  start-page: 252
  year: 1991
  end-page: 264
  ident: b8
  article-title: Small sample size effects in statistical pattern recognition: Recommendations for practitioners
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– reference: M. Jiang, Z. Wang, Q. Dou, Harmofl: Harmonizing local and global drifts in federated learning on heterogeneous medical images, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36, 2022, pp. 1087–1095.
– volume: 268
  start-page: 1
  year: 2023
  end-page: 12
  ident: b38
  article-title: DomainATM: Domain adaptation toolbox for medical data analysis
  publication-title: Neuroimage
– volume: 77
  start-page: 329
  year: 2018
  end-page: 353
  ident: b69
  article-title: Multiple instance learning: A survey of problem characteristics and applications
  publication-title: Pattern Recognit.
– start-page: 133
  year: 2019
  end-page: 141
  ident: b90
  article-title: Privacy-preserving federated brain tumour segmentation
  publication-title: Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10
– volume: 32
  start-page: 1
  year: 2020
  end-page: 10
  ident: b141
  article-title: PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
  publication-title: Data Brief
– volume: 27
  start-page: 790
  year: 2023
  end-page: 803
  ident: b26
  article-title: Handling privacy-sensitive medical data with federated learning: Challenges and future directions
  publication-title: IEEE J. Biomed. Health Inf.
– year: 2015
  ident: b144
  article-title: Diabetic retinopathy detection
– reference: A. Xu, W. Li, P. Guo, D. Yang, H.R. Roth, A. Hatamizadeh, C. Zhao, D. Xu, H. Huang, Z. Xu, Closing the generalization gap of cross-silo federated medical image segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20866–20875.
– year: 2023
  ident: b147
  article-title: FedDP: Dual personalization in federated medical image segmentation
  publication-title: IEEE Trans. Med. Imaging
– volume: 33
  start-page: 12546
  year: 2020
  end-page: 12558
  ident: b56
  article-title: Contrastive learning of global and local features for medical image segmentation with limited annotations
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 54
  start-page: 280
  year: 2019
  end-page: 296
  ident: b2
  article-title: Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
  publication-title: Med. Image Anal.
– reference: Q. Yang, J. Zhang, W. Hao, G.P. Spell, L. Carin, Flop: Federated learning on medical datasets using partial networks, in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 3845–3853.
– year: 2022
  ident: b171
  article-title: Towards personalized federated learning
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– year: 2022
  ident: b169
  article-title: Privacy and robustness in federated learning: Attacks and defenses
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– start-page: 336
  year: 2021
  end-page: 346
  ident: b138
  article-title: FedPerl: Semi-supervised peer learning for skin lesion classification
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– reference: X. Li, K. Huang, W. Yang, S. Wang, Z. Zhang, On the Convergence of FedAvg on Non-IID Data, in: Proceedings of International Conference on Learning Representations, 2020, pp. 1–12.
– year: 2023
  ident: b151
  article-title: Federated multi-organ segmentation with inconsistent labels
  publication-title: IEEE Trans. Med. Imaging
– reference: T. Li, A.K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, V. Smith, Federated optimization in heterogeneous networks, in: Proceedings of Machine Learning and Systems, Vol. 2, 2020, pp. 429–450.
– volume: 30
  year: 2017
  ident: b63
  article-title: Federated multi-task learning
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 21
  start-page: 167
  year: 2020
  ident: b99
  article-title: Open-source federated learning frameworks for IoT: A comparative review and analysis
  publication-title: Sensors
– start-page: 168
  year: 2018
  end-page: 172
  ident: b143
  article-title: Skin lesion analysis toward melanoma detection: A challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC)
  publication-title: 2018 IEEE 15th International Symposium on Biomedical Imaging
– start-page: 1
  year: 2023
  end-page: 5
  ident: b77
  article-title: Dealing with heterogeneous 3D MR knee images: A federated few-shot learning method with dual knowledge distillation
  publication-title: 2023 IEEE 20th International Symposium on Biomedical Imaging
– start-page: 1
  year: 2019
  end-page: 37
  ident: b121
  article-title: OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease
  publication-title: MedRxiv
– volume: 216
  year: 2021
  ident: b19
  article-title: A survey on federated learning
  publication-title: Knowl.-Based Syst.
– volume: Vol. 10
  start-page: 1
  year: 2022
  end-page: 16
  ident: b136
  article-title: BUSIS: A benchmark for breast ultrasound image segmentation
  publication-title: Healthcare
– volume: 15
  start-page: 869
  year: 2005
  end-page: 877
  ident: b101
  article-title: The Alzheimer’s disease neuroimaging initiative
  publication-title: Neuroimaging Clin.
– start-page: 673
  year: 2022
  end-page: 683
  ident: b32
  article-title: Suppressing poisoning attacks on federated learning for medical imaging
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– start-page: 1
  year: 2021
  end-page: 6
  ident: b85
  article-title: A federated deep learning framework for 3D brain MRI images
  publication-title: 2021 International Joint Conference on Neural Networks
– volume: 2
  year: 2020
  ident: b117
  article-title: fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning
  publication-title: Radiol.: Artif. Intell.
– volume: 42
  year: 2023
  ident: b41
  article-title: Specificity-preserving federated learning for MR image reconstruction
  publication-title: IEEE Trans. Med. Imaging
– volume: 10
  start-page: 1
  year: 2020
  end-page: 12
  ident: b110
  article-title: Covid-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
  publication-title: Sci. Rep.
– start-page: 191
  year: 2021
  end-page: 195
  ident: b119
  article-title: MedMNIST classification decathlon: A lightweight automl benchmark for medical image analysis
  publication-title: 2021 IEEE 18th International Symposium on Biomedical Imaging
– volume: 15
  start-page: 273
  year: 2002
  end-page: 289
  ident: b162
  article-title: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
  publication-title: NeuroImage
– volume: 34
  year: 2023
  ident: b70
  article-title: Learning from noisy labels with deep neural networks: A survey
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– volume: 14
  start-page: 1
  year: 2019
  end-page: 20
  ident: b9
  article-title: Machine learning algorithm validation with a limited sample size
  publication-title: PLOS ONE
– volume: 21
  start-page: 16301
  year: 2021
  end-page: 16314
  ident: b182
  article-title: Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging
  publication-title: IEEE Sens. J.
– year: 2022
  ident: b64
  article-title: Federated multi-task learning for joint diagnosis of multiple mental disorders on MRI scans
  publication-title: IEEE Trans. Biomed. Eng.
– year: 2019
  ident: b139
  article-title: Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC)
– volume: 27
  start-page: 1735
  year: 2021
  end-page: 1743
  ident: b100
  article-title: Federated learning for predicting clinical outcomes in patients with COVID-19
  publication-title: Nature Med.
– volume: 34
  start-page: 1993
  year: 2014
  end-page: 2024
  ident: b104
  article-title: The multimodal brain tumor image segmentation benchmark (BRATS)
  publication-title: IEEE Trans. Med. Imaging
– volume: 55
  start-page: 1
  year: 2022
  end-page: 37
  ident: b23
  article-title: Federated learning for smart healthcare: A survey
  publication-title: ACM Comput. Surv.
– year: 2023
  ident: b132
  article-title: Federated fusion of magnified histopathological images for breast tumor classification in the internet of medical things
  publication-title: IEEE J. Biomed. Health Inf.
– year: 2023
  ident: b145
  article-title: Federated semi-supervised learning for medical image segmentation via pseudo-label denoising
  publication-title: IEEE J. Biomed. Health Inf.
– start-page: 309
  year: 2022
  end-page: 319
  ident: b154
  article-title: Intervention & interaction federated abnormality detection with noisy clients
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 33
  start-page: 21394
  year: 2020
  end-page: 21405
  ident: b40
  article-title: Personalized federated learning with moreau envelopes
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 42
  year: 2023
  ident: b180
  article-title: A dataset auditing method for collaboratively trained machine learning models
  publication-title: IEEE Trans. Med. Imaging
– start-page: 695
  year: 2022
  end-page: –704
  ident: b49
  article-title: FedHarmony: Unlearning scanner bias with distributed data
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 65
  start-page: 1
  year: 2020
  end-page: 19
  ident: b179
  article-title: Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
  publication-title: Med. Image Anal.
– start-page: 129
  year: 2020
  end-page: 139
  ident: b50
  article-title: Siloed federated learning for multi-centric histopathology datasets
  publication-title: MICCAI Workshop on Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning (DART), Lima, Peru, October 4–8, 2020
– volume: 11
  start-page: 10708
  year: 2023
  end-page: 10722
  ident: b170
  article-title: Poisoning attacks in federated learning: A survey
  publication-title: IEEE Access
– volume: 42
  year: 2023
  ident: b81
  article-title: Fedni: Federated graph learning with network inpainting for population-based disease prediction
  publication-title: IEEE Trans. Med. Imaging
– start-page: 2560
  year: 2016
  end-page: 2567
  ident: b133
  article-title: Breast cancer histopathological image classification using convolutional neural networks
  publication-title: 2016 International Joint Conference on Neural Networks
– volume: 14
  start-page: 5510
  year: 2023
  ident: b80
  article-title: Mining multi-center heterogeneous medical data with distributed synthetic learning
  publication-title: Nature Commun.
– volume: 5
  start-page: 44
  year: 2018
  end-page: 53
  ident: b65
  article-title: A brief introduction to weakly supervised learning
  publication-title: Natl. Sci. Rev.
– volume: 70
  year: 2021
  ident: b72
  article-title: Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
  publication-title: Med. Image Anal.
– volume: 2
  start-page: 1
  year: 2020
  end-page: 8
  ident: b105
  article-title: Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge
  publication-title: Radiol.: Artif. Intell.
– volume: 11
  start-page: 28628
  year: 2023
  end-page: 28644
  ident: b27
  article-title: A systematic review on federated learning in medical image analysis
  publication-title: IEEE Access
– volume: 10
  year: 2021
  ident: b28
  article-title: Ethical issues in patient data ownership
  publication-title: Interact. J. Med. Res.
– reference: P. Guo, P. Wang, J. Zhou, S. Jiang, V.M. Patel, Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 2423–2432.
– volume: 45
  start-page: 1113
  year: 2013
  end-page: 1120
  ident: b116
  article-title: The cancer genome atlas pan-cancer analysis project
  publication-title: Nature Genet.
– volume: 8
  start-page: 132665
  year: 2020
  end-page: 132676
  ident: b109
  article-title: Can AI help in screening viral and COVID-19 pneumonia?
  publication-title: IEEE Access
– start-page: 256
  year: 2022
  end-page: 265
  ident: b124
  article-title: Contrastive re-localization and history distillation in federated CMR segmentation
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 55
  start-page: 1
  year: 2023
  end-page: 31
  ident: b181
  article-title: Blockchain-empowered federated learning: Challenges, solutions, and future directions
  publication-title: ACM Comput. Surv.
– volume: 13
  start-page: 1
  year: 2022
  end-page: 23
  ident: b21
  article-title: Federated learning for healthcare: Systematic review and architecture proposal
  publication-title: ACM Trans. Intell. Syst. Technol.
– start-page: 1
  year: 2021
  end-page: 20
  ident: b15
  article-title: A survey on federated learning systems: Vision, hype and reality for data privacy and protection
  publication-title: IEEE Trans. Knowl. Data Eng.
– start-page: 1
  year: 2011
  end-page: 6
  ident: b146
  article-title: RIM-ONE: An open retinal image database for optic nerve evaluation
  publication-title: 24th International Symposium on Computer-Based Medical Systems
– volume: 3
  start-page: 119
  year: 2020
  ident: b25
  article-title: The future of digital health with federated learning
  publication-title: NPJ Digit. Med.
– reference: J.-Y. Zhu, T. Park, P. Isola, A.A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2223–2232.
– volume: 67
  year: 2022
  ident: b97
  article-title: OpenFL: The open federated learning library
  publication-title: Phys. Med. Biol.
– reference: J. Irvin, et al., Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 590–597.
– volume: 8
  start-page: 15884
  year: 2021
  end-page: 15891
  ident: b31
  article-title: Dynamic-fusion-based federated learning for COVID-19 detection
  publication-title: IEEE Internet Things J.
– volume: 42
  year: 2023
  ident: b45
  article-title: IOP-FL: Inside-outside personalization for federated medical image segmentation
  publication-title: IEEE Trans. Med. Imaging
– start-page: 357
  year: 2021
  end-page: 366
  ident: b156
  article-title: Federated whole prostate segmentation in MRI with personalized neural architectures
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 9
  start-page: 124682
  year: 2021
  end-page: 124700
  ident: b18
  article-title: Challenges, applications and design aspects of federated learning: A survey
  publication-title: IEEE Access
– start-page: 1191
  year: 2021
  end-page: 1195
  ident: b36
  article-title: Scaling neuroscience research using federated learning
  publication-title: 2021 IEEE 18th International Symposium on Biomedical Imaging
– year: 2019
  ident: b157
  article-title: A large annotated medical image dataset for the development and evaluation of segmentation algorithms
– volume: 42
  year: 2023
  ident: b74
  article-title: Federated cycling (FedCy): Semi-supervised Federated Learning of surgical phases
  publication-title: IEEE Trans. Med. Imaging
– volume: 65
  start-page: 1
  year: 2020
  end-page: 14
  ident: b48
  article-title: Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
  publication-title: Med. Image Anal.
– start-page: 111
  year: 2021
  end-page: 139
  ident: b95
  article-title: Pysyft: A library for easy federated learning
  publication-title: Federated Learning Systems: Towards Next-Generation AI
– volume: 19
  start-page: 659
  year: 2014
  end-page: 667
  ident: b103
  article-title: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
  publication-title: Mol. Psychiatry
– start-page: 159
  year: 2020
  end-page: 168
  ident: b131
  article-title: Federated simulation for medical imaging
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 26
  start-page: 5596
  year: 2022
  end-page: 5607
  ident: b39
  article-title: Customized federated learning for multi-source decentralized medical image classification
  publication-title: IEEE J. Biomed. Health Inf.
– start-page: 201
  year: 2020
  end-page: 210
  ident: b98
  article-title: Fed-biomed: A general open-source frontend framework for federated learning in healthcare
  publication-title: Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020
– year: 2023
  ident: b33
  article-title: Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging
  publication-title: IEEE Trans. Med. Imaging
– volume: 75
  start-page: 1
  year: 2022
  end-page: 15
  ident: b114
  article-title: Analysis of the ISIC image datasets: Usage, benchmarks and recommendations
  publication-title: Med. Image Anal.
– start-page: 92
  year: 2019
  end-page: 104
  ident: b126
  article-title: Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation
  publication-title: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018
– volume: 27
  start-page: 685
  year: 2008
  end-page: 691
  ident: b102
  article-title: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
  publication-title: J. Magn. Reson. Imaging
– start-page: 740
  year: 2014
  end-page: 755
  ident: b7
  article-title: Microsoft COCO: Common objects in context
  publication-title: European Conference on Computer Vision
– year: 2015
  ident: b75
  article-title: Distilling the knowledge in a neural network
– start-page: 325
  year: 2021
  end-page: 335
  ident: b125
  article-title: Federated semi-supervised medical image classification via inter-client relation matching
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– year: 2023
  ident: b149
  article-title: FedDM: Federated weakly supervised segmentation via annotation calibration and gradient de-conflicting
  publication-title: IEEE Trans. Med. Imaging
– volume: 43
  start-page: 3614
  year: 2020
  end-page: 3631
  ident: b177
  article-title: Recent advances in open set recognition: A survey
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– reference: K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729–9738.
– volume: 10
  start-page: 1
  year: 2019
  end-page: 19
  ident: b17
  article-title: Federated machine learning: Concept and applications
  publication-title: ACM Trans. Intell. Syst. Technol.
– volume: 21
  start-page: 1
  year: 2021
  end-page: 31
  ident: b24
  article-title: Federated learning in a medical context: A systematic literature review
  publication-title: ACM Trans. Internet Technol. (TOIT)
– start-page: 728
  year: 2022
  end-page: 738
  ident: b78
  article-title: Federated medical image analysis with virtual sample synthesis
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 69
  start-page: 1173
  year: 2022
  end-page: 1185
  ident: b3
  article-title: Domain adaptation for medical image analysis: A survey
  publication-title: IEEE Trans. Biomed. Eng.
– start-page: 378
  year: 2021
  end-page: 387
  ident: b61
  article-title: Federated contrastive learning for decentralized unlabeled medical images
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 42
  year: 2023
  ident: b120
  article-title: Federated brain graph evolution prediction using decentralized connectivity datasets with temporally-varying acquisitions
  publication-title: IEEE Trans. Med. Imaging
– volume: 21
  year: 2019
  ident: b30
  article-title: Google is fined $57 million under Europe’s data privacy law
  publication-title: N.Y. Times
– volume: 5
  start-page: 1
  year: 2018
  end-page: 9
  ident: b113
  article-title: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
  publication-title: Sci. Data
– volume: 72
  year: 2021
  ident: b175
  article-title: Autoencoder based self-supervised test-time adaptation for medical image analysis
  publication-title: Med. Image Anal.
– start-page: 367
  year: 2021
  end-page: 377
  ident: b60
  article-title: Federated contrastive learning for volumetric medical image segmentation
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– volume: 42
  start-page: 60
  year: 2017
  end-page: 88
  ident: b4
  article-title: A survey on deep learning in medical image analysis
  publication-title: Med. Image Anal.
– volume: 23
  start-page: 538
  year: 2018
  end-page: 546
  ident: b140
  article-title: Seven-point checklist and skin lesion classification using multitask multimodal neural nets
  publication-title: IEEE J. Biomed. Health Inf.
– year: 2020
  ident: b10
  article-title: HIPAA
– volume: 9
  start-page: 211
  year: 2014
  end-page: 407
  ident: b91
  article-title: The algorithmic foundations of differential privacy
  publication-title: Found. Trends® Theor. Comput. Sci.
– volume: 32
  start-page: 18069
  year: 2020
  end-page: 18083
  ident: b186
  article-title: The importance of interpretability and visualization in machine learning for applications in medicine and health care
  publication-title: Neural Comput. Appl.
– volume: 35
  year: 2023
  ident: b174
  article-title: Generalizing to unseen domains: A survey on domain generalization
  publication-title: IEEE Trans. Knowl. Data Eng.
– start-page: 1077
  year: 2021
  end-page: 1081
  ident: b43
  article-title: Federated learning for site aware chest radiograph screening
  publication-title: 2021 IEEE 18th International Symposium on Biomedical Imaging
– volume: 37
  start-page: 2514
  year: 2018
  end-page: 2525
  ident: b111
  article-title: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
  publication-title: IEEE Trans. Med. Imaging
– year: 2018
  ident: b29
  article-title: CCPA
– volume: 40
  start-page: 2306
  year: 2021
  end-page: 2317
  ident: b118
  article-title: Results of the 2020 fastMRI challenge for machine learning MR image reconstruction
  publication-title: IEEE Trans. Med. Imaging
– year: 2019
  ident: b11
  article-title: GDPR
– reference: K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konečnỳ, S. Mazzocchi, B. McMahan, et al., Towards federated learning at scale: System design, in: Proceedings of Machine Learning and Systems, Vol. 1, 2019, pp. 374–388.
– volume: 10
  start-page: 213
  year: 2017
  end-page: 234
  ident: b68
  article-title: Multiple-instance learning for medical image and video analysis
  publication-title: IEEE Rev. Biomed. Eng.
– year: 2022
  ident: b178
  article-title: Uncertainty-aware aggregation for federated open set domain adaptation
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– year: 2023
  ident: b187
  article-title: Towards interpretable federated learning
– volume: 31
  year: 2018
  ident: b83
  article-title: Frequency-domain dynamic pruning for convolutional neural networks
  publication-title: Adv. Neural Inf. Process. Syst.
– start-page: 234
  year: 2015
  end-page: 241
  ident: b37
  article-title: U-net: Convolutional networks for biomedical image segmentation
  publication-title: 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, October 5-9, 2015
– volume: 45
  start-page: 4396
  year: 2023
  end-page: 4415
  ident: b173
  article-title: Domain generalization: A survey
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– start-page: 1273
  year: 2017
  end-page: 1282
  ident: b12
  article-title: Communication-efficient learning of deep networks from decentralized data
  publication-title: Artificial Intelligence and Statistics
– start-page: 14
  year: 2022
  end-page: 23
  ident: b47
  article-title: Federated stain normalization for computational pathology
  publication-title: International Conference on Medical Image Computing and Computer-Assisted Intervention
– start-page: 248
  year: 2009
  end-page: 255
  ident: b6
  article-title: Imagenet: A large-scale hierarchical image database
  publication-title: 2009 IEEE Conference on Computer Vision and Pattern Recognition
– start-page: 953
  year: 2021
  end-page: 956
  ident: b46
  article-title: Style normalization in histology with federated learning
  publication-title: 2021 IEEE 18th International Symposium on Biomedical Imaging
– volume: 81
  year: 2022
  ident: b59
  article-title: Distributed contrastive learning for medical image segmentation
  publication-title: Med. Image Anal.
– volume: 109
  start-page: 373
  year: 2020
  end-page: 440
  ident: b67
  article-title: A survey on semi-supervised learning
  publication-title: Mach. Learn.
– start-page: 1
  year: 2022
  end-page: 31
  ident: b122
  article-title: Effectiveness of federated learning and CNN ensemble architectures for identifying brain tumors using MRI images
  publication-title: Neural Process. Lett.
– volume: 9
  start-page: 116869
  year: 2021
  end-page: 116878
  ident: b96
  article-title: Benchmarking PySyft federated learning framework on MIMIC-III dataset
  publication-title: IEEE Access
– start-page: 325
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b125
  article-title: Federated semi-supervised medical image classification via inter-client relation matching
– ident: 10.1016/j.patcog.2024.110424_b55
  doi: 10.1609/aaai.v36i1.19993
– volume: 109
  start-page: 373
  issue: 2
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b67
  article-title: A survey on semi-supervised learning
  publication-title: Mach. Learn.
  doi: 10.1007/s10994-019-05855-6
– volume: 5
  start-page: 44
  issue: 1
  year: 2018
  ident: 10.1016/j.patcog.2024.110424_b65
  article-title: A brief introduction to weakly supervised learning
  publication-title: Natl. Sci. Rev.
  doi: 10.1093/nsr/nwx106
– volume: 27
  start-page: 685
  issue: 4
  year: 2008
  ident: 10.1016/j.patcog.2024.110424_b102
  article-title: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
  publication-title: J. Magn. Reson. Imaging
  doi: 10.1002/jmri.21049
– volume: 34
  start-page: 1993
  issue: 10
  year: 2014
  ident: 10.1016/j.patcog.2024.110424_b104
  article-title: The multimodal brain tumor image segmentation benchmark (BRATS)
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2014.2377694
– volume: 42
  issue: 7
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b161
  article-title: Federated learning of generative image priors for MRI reconstruction
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2022.3220757
– volume: 33
  start-page: 12546
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b56
  article-title: Contrastive learning of global and local features for medical image segmentation with limited annotations
  publication-title: Adv. Neural Inf. Process. Syst.
– ident: 10.1016/j.patcog.2024.110424_b57
  doi: 10.1109/CVPR42600.2020.00975
– volume: 42
  issue: 7
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b74
  article-title: Federated cycling (FedCy): Semi-supervised Federated Learning of surgical phases
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2022.3222126
– year: 2023
  ident: 10.1016/j.patcog.2024.110424_b151
  article-title: Federated multi-organ segmentation with inconsistent labels
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2023.3270140
– year: 2015
  ident: 10.1016/j.patcog.2024.110424_b144
– start-page: 2560
  year: 2016
  ident: 10.1016/j.patcog.2024.110424_b133
  article-title: Breast cancer histopathological image classification using convolutional neural networks
– start-page: 1
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b77
  article-title: Dealing with heterogeneous 3D MR knee images: A federated few-shot learning method with dual knowledge distillation
– volume: 77
  start-page: 329
  year: 2018
  ident: 10.1016/j.patcog.2024.110424_b69
  article-title: Multiple instance learning: A survey of problem characteristics and applications
  publication-title: Pattern Recognit.
  doi: 10.1016/j.patcog.2017.10.009
– year: 2023
  ident: 10.1016/j.patcog.2024.110424_b187
– start-page: 673
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b32
  article-title: Suppressing poisoning attacks on federated learning for medical imaging
– volume: 268
  start-page: 1
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b38
  article-title: DomainATM: Domain adaptation toolbox for medical data analysis
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2023.119863
– volume: 34
  start-page: 5586
  issue: 12
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b62
  article-title: A survey on multi-task learning
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2021.3070203
– year: 2023
  ident: 10.1016/j.patcog.2024.110424_b132
  article-title: Federated fusion of magnified histopathological images for breast tumor classification in the internet of medical things
  publication-title: IEEE J. Biomed. Health Inf.
– ident: 10.1016/j.patcog.2024.110424_b168
– start-page: 695
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b49
  article-title: FedHarmony: Unlearning scanner bias with distributed data
– year: 2022
  ident: 10.1016/j.patcog.2024.110424_b169
  article-title: Privacy and robustness in federated learning: Attacks and defenses
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– volume: 43
  start-page: 3614
  issue: 10
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b177
  article-title: Recent advances in open set recognition: A survey
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/TPAMI.2020.2981604
– volume: 54
  start-page: 1
  issue: 6
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b20
  article-title: A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
  publication-title: ACM Comput. Surv.
  doi: 10.1145/3460427
– volume: 83
  start-page: 242
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b1
  article-title: Artificial intelligence and machine learning for medical imaging: A technology review
  publication-title: Phys. Medica
  doi: 10.1016/j.ejmp.2021.04.016
– volume: 54
  start-page: 280
  year: 2019
  ident: 10.1016/j.patcog.2024.110424_b2
  article-title: Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2019.03.009
– volume: 21
  year: 2019
  ident: 10.1016/j.patcog.2024.110424_b30
  article-title: Google is fined $57 million under Europe’s data privacy law
  publication-title: N.Y. Times
– ident: 10.1016/j.patcog.2024.110424_b35
  doi: 10.1145/3447548.3467185
– ident: 10.1016/j.patcog.2024.110424_b54
  doi: 10.1109/ICCV.2017.244
– volume: 81
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b59
  article-title: Distributed contrastive learning for medical image segmentation
  publication-title: Med. Image Anal.
– start-page: 728
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b78
  article-title: Federated medical image analysis with virtual sample synthesis
– volume: 15
  start-page: 869
  issue: 4
  year: 2005
  ident: 10.1016/j.patcog.2024.110424_b101
  article-title: The Alzheimer’s disease neuroimaging initiative
  publication-title: Neuroimaging Clin.
  doi: 10.1016/j.nic.2005.09.008
– volume: 40
  start-page: 3543
  issue: 12
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b112
  article-title: Multi-centre, multi-vendor and multi-disease cardiac segmentation: The M&Ms challenge
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2021.3090082
– volume: 2
  start-page: 1
  issue: 3
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b105
  article-title: Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge
  publication-title: Radiol.: Artif. Intell.
– volume: 65
  start-page: 1
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b179
  article-title: Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2020.101759
– year: 2019
  ident: 10.1016/j.patcog.2024.110424_b11
– volume: 21
  start-page: 167
  issue: 1
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b99
  article-title: Open-source federated learning frameworks for IoT: A comparative review and analysis
  publication-title: Sensors
  doi: 10.3390/s21010167
– start-page: 1273
  year: 2017
  ident: 10.1016/j.patcog.2024.110424_b12
  article-title: Communication-efficient learning of deep networks from decentralized data
– year: 2022
  ident: 10.1016/j.patcog.2024.110424_b64
  article-title: Federated multi-task learning for joint diagnosis of multiple mental disorders on MRI scans
  publication-title: IEEE Trans. Biomed. Eng.
– volume: 21
  start-page: 16301
  issue: 14
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b182
  article-title: Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging
  publication-title: IEEE Sens. J.
  doi: 10.1109/JSEN.2021.3076767
– volume: 42
  issue: 7
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b45
  article-title: IOP-FL: Inside-outside personalization for federated medical image segmentation
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2023.3263072
– volume: 32
  year: 2019
  ident: 10.1016/j.patcog.2024.110424_b89
  article-title: Deep leakage from gradients
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 23
  start-page: 538
  issue: 2
  year: 2018
  ident: 10.1016/j.patcog.2024.110424_b140
  article-title: Seven-point checklist and skin lesion classification using multitask multimodal neural nets
  publication-title: IEEE J. Biomed. Health Inf.
  doi: 10.1109/JBHI.2018.2824327
– ident: 10.1016/j.patcog.2024.110424_b88
  doi: 10.1109/CVPR46437.2021.01607
– start-page: 201
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b98
  article-title: Fed-biomed: A general open-source frontend framework for federated learning in healthcare
– start-page: 336
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b138
  article-title: FedPerl: Semi-supervised peer learning for skin lesion classification
– volume: 216
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b19
  article-title: A survey on federated learning
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2021.106775
– volume: 21
  start-page: 1
  issue: 2
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b24
  article-title: Federated learning in a medical context: A systematic literature review
  publication-title: ACM Trans. Internet Technol. (TOIT)
  doi: 10.1145/3412357
– volume: 18
  start-page: 5648
  issue: 8
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b76
  article-title: Medisecfed: private and secure medical image classification in the presence of malicious clients
  publication-title: IEEE Trans. Ind. Inform.
  doi: 10.1109/TII.2021.3138919
– year: 2022
  ident: 10.1016/j.patcog.2024.110424_b171
  article-title: Towards personalized federated learning
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– start-page: 1
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b183
  article-title: Blockchain for medical collaboration: A federated learning-based approach for multi-class respiratory disease classification
  publication-title: Healthc. Anal.
– ident: 10.1016/j.patcog.2024.110424_b108
  doi: 10.1109/CVPR.2017.369
– start-page: 347
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b142
  article-title: Personalized retrogress-resilient framework for real-world medical federated learning
– start-page: 129
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b50
  article-title: Siloed federated learning for multi-centric histopathology datasets
– volume: 9
  start-page: 124682
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b18
  article-title: Challenges, applications and design aspects of federated learning: A survey
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3111118
– volume: 42
  issue: 7
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b81
  article-title: Fedni: Federated graph learning with network inpainting for population-based disease prediction
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2022.3188728
– ident: 10.1016/j.patcog.2024.110424_b44
  doi: 10.1109/CVPR52688.2022.02020
– start-page: 1
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b86
  article-title: Fedsld: Federated learning with shared label distribution for medical image classification
– volume: 26
  start-page: 4635
  issue: 9
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b42
  article-title: SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging
  publication-title: IEEE J. Biomed. Health Inf.
  doi: 10.1109/JBHI.2022.3185956
– start-page: 953
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b46
  article-title: Style normalization in histology with federated learning
– start-page: 1
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b85
  article-title: A federated deep learning framework for 3D brain MRI images
– volume: 13
  start-page: 1
  issue: 4
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b21
  article-title: Federated learning for healthcare: Systematic review and architecture proposal
  publication-title: ACM Trans. Intell. Syst. Technol.
  doi: 10.1145/3501813
– volume: 22
  start-page: 1218
  issue: 4
  year: 2017
  ident: 10.1016/j.patcog.2024.110424_b137
  article-title: Automated breast ultrasound lesions detection using convolutional neural networks
  publication-title: IEEE J. Biomed. Health Inform.
  doi: 10.1109/JBHI.2017.2731873
– volume: 3
  start-page: 119
  issue: 1
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b25
  article-title: The future of digital health with federated learning
  publication-title: NPJ Digit. Med.
  doi: 10.1038/s41746-020-00323-1
– volume: 9
  start-page: 1
  issue: 1
  year: 2019
  ident: 10.1016/j.patcog.2024.110424_b160
  article-title: Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-018-37257-4
– volume: 35
  issue: 8
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b174
  article-title: Generalizing to unseen domains: A survey on domain generalization
  publication-title: IEEE Trans. Knowl. Data Eng.
– volume: 11
  start-page: 28628
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b27
  article-title: A systematic review on federated learning in medical image analysis
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2023.3260027
– volume: 12
  start-page: 1953
  issue: 1
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b159
  article-title: Federated learning and differential privacy for medical image analysis
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-022-05539-7
– ident: 10.1016/j.patcog.2024.110424_b13
– volume: 27
  start-page: 790
  issue: 2
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b26
  article-title: Handling privacy-sensitive medical data with federated learning: Challenges and future directions
  publication-title: IEEE J. Biomed. Health Inf.
  doi: 10.1109/JBHI.2022.3185673
– volume: 34
  issue: 11
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b70
  article-title: Learning from noisy labels with deep neural networks: A survey
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2022.3152527
– year: 2022
  ident: 10.1016/j.patcog.2024.110424_b66
  article-title: A survey on deep semi-supervised learning
  publication-title: IEEE Trans. Knowl. Data Eng.
– start-page: 92
  year: 2019
  ident: 10.1016/j.patcog.2024.110424_b126
  article-title: Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation
– volume: 9
  start-page: 211
  issue: 3–4
  year: 2014
  ident: 10.1016/j.patcog.2024.110424_b91
  article-title: The algorithmic foundations of differential privacy
  publication-title: Found. Trends® Theor. Comput. Sci.
– start-page: 1
  year: 2019
  ident: 10.1016/j.patcog.2024.110424_b121
  article-title: OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease
  publication-title: MedRxiv
– year: 2018
  ident: 10.1016/j.patcog.2024.110424_b29
– ident: 10.1016/j.patcog.2024.110424_b92
– volume: 70
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b72
  article-title: Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2021.101992
– volume: 55
  start-page: 1
  issue: 3
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b23
  article-title: Federated learning for smart healthcare: A survey
  publication-title: ACM Comput. Surv.
  doi: 10.1145/3501296
– volume: 29
  start-page: 135
  issue: 1
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b158
  article-title: Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer
  publication-title: Nature Med.
  doi: 10.1038/s41591-022-02155-w
– volume: 10
  issue: 2
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b28
  article-title: Ethical issues in patient data ownership
  publication-title: Interact. J. Med. Res.
  doi: 10.2196/22269
– volume: 25
  start-page: 2615
  issue: 7
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b53
  article-title: Variation-aware federated learning with multi-source decentralized medical image data
  publication-title: IEEE J. Biomed. Health Inf.
  doi: 10.1109/JBHI.2020.3040015
– volume: 29
  start-page: 120
  issue: 1
  year: 2009
  ident: 10.1016/j.patcog.2024.110424_b166
  article-title: Computer-aided detection of polyps in CT colonography using logistic regression
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2009.2028576
– volume: 42
  start-page: 60
  year: 2017
  ident: 10.1016/j.patcog.2024.110424_b4
  article-title: A survey on deep learning in medical image analysis
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2017.07.005
– volume: 106
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b128
  article-title: Federated learning for COVID-19 screening from Chest X-ray images
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2021.107330
– volume: 13
  start-page: 252
  issue: 3
  year: 1991
  ident: 10.1016/j.patcog.2024.110424_b8
  article-title: Small sample size effects in statistical pattern recognition: Recommendations for practitioners
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/34.75512
– volume: 5
  start-page: 1
  issue: 1
  year: 2018
  ident: 10.1016/j.patcog.2024.110424_b113
  article-title: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
  publication-title: Sci. Data
  doi: 10.1038/sdata.2018.161
– year: 2023
  ident: 10.1016/j.patcog.2024.110424_b33
  article-title: Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2022.3233574
– volume: 18
  start-page: 359
  issue: 2
  year: 2014
  ident: 10.1016/j.patcog.2024.110424_b115
  article-title: Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2013.12.002
– ident: 10.1016/j.patcog.2024.110424_b51
  doi: 10.1109/CVPR46437.2021.00245
– volume: 37
  start-page: 50
  issue: 3
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b16
  article-title: Federated learning: Challenges, methods, and future directions
  publication-title: IEEE Signal Process. Mag.
  doi: 10.1109/MSP.2020.2975749
– volume: 28
  start-page: 1
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b135
  article-title: Dataset of breast ultrasound images
  publication-title: Data Brief
  doi: 10.1016/j.dib.2019.104863
– volume: 10
  start-page: 1
  issue: 1
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b110
  article-title: Covid-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
  publication-title: Sci. Rep.
– volume: 15
  start-page: 273
  issue: 1
  year: 2002
  ident: 10.1016/j.patcog.2024.110424_b162
  article-title: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
  publication-title: NeuroImage
  doi: 10.1006/nimg.2001.0978
– start-page: 740
  year: 2014
  ident: 10.1016/j.patcog.2024.110424_b7
  article-title: Microsoft COCO: Common objects in context
– year: 2023
  ident: 10.1016/j.patcog.2024.110424_b149
  article-title: FedDM: Federated weakly supervised segmentation via annotation calibration and gradient de-conflicting
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2023.3235757
– volume: 19
  start-page: 659
  issue: 6
  year: 2014
  ident: 10.1016/j.patcog.2024.110424_b103
  article-title: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
  publication-title: Mol. Psychiatry
  doi: 10.1038/mp.2013.78
– volume: 14
  start-page: 5510
  issue: 1
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b80
  article-title: Mining multi-center heterogeneous medical data with distributed synthetic learning
  publication-title: Nature Commun.
  doi: 10.1038/s41467-023-40687-y
– start-page: 191
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b119
  article-title: MedMNIST classification decathlon: A lightweight automl benchmark for medical image analysis
– volume: 84
  start-page: 1
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b152
  article-title: The liver tumor segmentation benchmark (lits)
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2022.102680
– start-page: 428
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b176
  article-title: Test-time unsupervised domain adaptation
– start-page: 256
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b124
  article-title: Contrastive re-localization and history distillation in federated CMR segmentation
– volume: 2
  issue: 1
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b117
  article-title: fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning
  publication-title: Radiol.: Artif. Intell.
– volume: 75
  start-page: 1
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b114
  article-title: Analysis of the ISIC image datasets: Usage, benchmarks and recommendations
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2021.102305
– volume: 30
  year: 2017
  ident: 10.1016/j.patcog.2024.110424_b63
  article-title: Federated multi-task learning
  publication-title: Adv. Neural Inf. Process. Syst.
– start-page: 67
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b127
  article-title: Learning underrepresented classes from decentralized partially labeled medical images
– volume: 14
  start-page: 1
  issue: 11
  year: 2019
  ident: 10.1016/j.patcog.2024.110424_b9
  article-title: Machine learning algorithm validation with a limited sample size
  publication-title: PLOS ONE
  doi: 10.1371/journal.pone.0224365
– volume: 31
  year: 2018
  ident: 10.1016/j.patcog.2024.110424_b83
  article-title: Frequency-domain dynamic pruning for convolutional neural networks
  publication-title: Adv. Neural Inf. Process. Syst.
– start-page: 378
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b61
  article-title: Federated contrastive learning for decentralized unlabeled medical images
– volume: 76
  start-page: 1
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b73
  article-title: Federated learning for computational pathology on gigapixel whole slide images
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2021.102298
– year: 2022
  ident: 10.1016/j.patcog.2024.110424_b178
  article-title: Uncertainty-aware aggregation for federated open set domain adaptation
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– volume: 9
  start-page: 116869
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b96
  article-title: Benchmarking PySyft federated learning framework on MIMIC-III dataset
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3105929
– volume: 42
  issue: 7
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b41
  article-title: Specificity-preserving federated learning for MR image reconstruction
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2022.3202106
– volume: 27
  start-page: 1735
  issue: 10
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b100
  article-title: Federated learning for predicting clinical outcomes in patients with COVID-19
  publication-title: Nature Med.
  doi: 10.1038/s41591-021-01506-3
– volume: 67
  issue: 21
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b97
  article-title: OpenFL: The open federated learning library
  publication-title: Phys. Med. Biol.
  doi: 10.1088/1361-6560/ac97d9
– year: 2022
  ident: 10.1016/j.patcog.2024.110424_b34
  article-title: Integrated CNN and federated learning for COVID-19 detection on chest X-ray images
  publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform.
– start-page: 1
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b122
  article-title: Effectiveness of federated learning and CNN ensemble architectures for identifying brain tumors using MRI images
  publication-title: Neural Process. Lett.
– volume: 12
  start-page: 3551
  issue: 1
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b129
  article-title: Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-022-07186-4
– start-page: 14
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b47
  article-title: Federated stain normalization for computational pathology
– volume: 78
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b52
  article-title: Handling data heterogeneity with generative replay in collaborative learning for medical imaging
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2022.102424
– volume: 25
  start-page: 845
  issue: 5
  year: 2013
  ident: 10.1016/j.patcog.2024.110424_b71
  article-title: Classification in the presence of label noise: A survey
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2013.2292894
– year: 2015
  ident: 10.1016/j.patcog.2024.110424_b75
– volume: 8
  start-page: 132665
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b109
  article-title: Can AI help in screening viral and COVID-19 pneumonia?
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2020.3010287
– volume: 32
  start-page: 18069
  issue: 24
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b186
  article-title: The importance of interpretability and visualization in machine learning for applications in medicine and health care
  publication-title: Neural Comput. Appl.
  doi: 10.1007/s00521-019-04051-w
– volume: 10
  start-page: 1
  issue: 2
  year: 2019
  ident: 10.1016/j.patcog.2024.110424_b17
  article-title: Federated machine learning: Concept and applications
  publication-title: ACM Trans. Intell. Syst. Technol.
  doi: 10.1145/3298981
– volume: 14
  start-page: 1
  issue: 1–2
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b14
  article-title: Advances and open problems in federated learning
  publication-title: Found. Trends® Mach. Learn.
  doi: 10.1561/2200000083
– start-page: 1191
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b36
  article-title: Scaling neuroscience research using federated learning
– start-page: 309
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b154
  article-title: Intervention & interaction federated abnormality detection with noisy clients
– start-page: 133
  year: 2019
  ident: 10.1016/j.patcog.2024.110424_b90
  article-title: Privacy-preserving federated brain tumour segmentation
– year: 2023
  ident: 10.1016/j.patcog.2024.110424_b94
  article-title: Do gradient inversion attacks make federated learning unsafe?
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2023.3239391
– year: 2021
  ident: 10.1016/j.patcog.2024.110424_b150
– volume: 5
  start-page: 89
  issue: 4
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b130
  article-title: Emidec: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac MRI
  publication-title: Data
  doi: 10.3390/data5040089
– start-page: 159
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b131
  article-title: Federated simulation for medical imaging
– volume: 41
  start-page: 1979
  issue: 8
  year: 2018
  ident: 10.1016/j.patcog.2024.110424_b79
  article-title: Virtual adversarial training: a regularization method for supervised and semi-supervised learning
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/TPAMI.2018.2858821
– start-page: 234
  year: 2015
  ident: 10.1016/j.patcog.2024.110424_b37
  article-title: U-net: Convolutional networks for biomedical image segmentation
– ident: 10.1016/j.patcog.2024.110424_b167
– volume: 42
  issue: 7
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b120
  article-title: Federated brain graph evolution prediction using decentralized connectivity datasets with temporally-varying acquisitions
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2022.3225083
– start-page: 1077
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b43
  article-title: Federated learning for site aware chest radiograph screening
– volume: 33
  start-page: 16937
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b87
  article-title: Inverting gradients-how easy is it to break privacy in federated learning?
  publication-title: Adv. Neural Inf. Process. Syst.
– year: 2020
  ident: 10.1016/j.patcog.2024.110424_b10
– volume: 33
  start-page: 21394
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b40
  article-title: Personalized federated learning with moreau envelopes
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 10
  start-page: 213
  year: 2017
  ident: 10.1016/j.patcog.2024.110424_b68
  article-title: Multiple-instance learning for medical image and video analysis
  publication-title: IEEE Rev. Biomed. Eng.
  doi: 10.1109/RBME.2017.2651164
– year: 2023
  ident: 10.1016/j.patcog.2024.110424_b134
  article-title: FedMix: Mixed supervised federated learning for medical image segmentation
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2022.3233405
– volume: 65
  start-page: 1
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b48
  article-title: Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2020.101765
– volume: 8
  start-page: 15884
  issue: 21
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b31
  article-title: Dynamic-fusion-based federated learning for COVID-19 detection
  publication-title: IEEE Internet Things J.
  doi: 10.1109/JIOT.2021.3056185
– volume: 33
  start-page: 1083
  issue: 5
  year: 2014
  ident: 10.1016/j.patcog.2024.110424_b153
  article-title: Computer-aided detection of prostate cancer in MRI
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2014.2303821
– volume: 3
  start-page: 473
  issue: 6
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b93
  article-title: End-to-end privacy preserving deep learning on multi-institutional medical imaging
  publication-title: Nat. Mach. Intell.
  doi: 10.1038/s42256-021-00337-8
– volume: 45
  start-page: 4396
  issue: 4
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b173
  article-title: Domain generalization: A survey
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– start-page: 181
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b185
  article-title: Federated learning for breast density classification: A real-world implementation
– volume: 33
  start-page: 8435
  issue: 14
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b163
  article-title: Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification
  publication-title: Neural Comput. Appl.
  doi: 10.1007/s00521-020-05596-x
– volume: 69
  start-page: 1173
  issue: 3
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b3
  article-title: Domain adaptation for medical image analysis: A survey
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2021.3117407
– volume: 37
  start-page: 2514
  issue: 11
  year: 2018
  ident: 10.1016/j.patcog.2024.110424_b111
  article-title: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2018.2837502
– volume: 45
  start-page: 1113
  issue: 10
  year: 2013
  ident: 10.1016/j.patcog.2024.110424_b116
  article-title: The cancer genome atlas pan-cancer analysis project
  publication-title: Nature Genet.
  doi: 10.1038/ng.2764
– volume: 5
  start-page: 1
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b22
  article-title: Cloud-based federated learning implementation across medical centers
  publication-title: JCO Clin. Cancer Inform.
  doi: 10.1200/CCI.20.00060
– volume: 3
  start-page: 1
  issue: 1
  year: 2016
  ident: 10.1016/j.patcog.2024.110424_b123
  article-title: A structural and functional magnetic resonance imaging dataset of brain tumour patients
  publication-title: Sci. Data
  doi: 10.1038/sdata.2016.3
– volume: 16
  start-page: 1
  issue: 8
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b155
  article-title: Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations
  publication-title: PLOS ONE
  doi: 10.1371/journal.pone.0255809
– year: 2023
  ident: 10.1016/j.patcog.2024.110424_b147
  article-title: FedDP: Dual personalization in federated medical image segmentation
  publication-title: IEEE Trans. Med. Imaging
– start-page: 248
  year: 2009
  ident: 10.1016/j.patcog.2024.110424_b6
  article-title: Imagenet: A large-scale hierarchical image database
– volume: 42
  issue: 7
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b84
  article-title: Proportionally fair hospital collaborations in federated learning of histopathology images
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2023.3234450
– volume: 11
  start-page: 10708
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b170
  article-title: Poisoning attacks in federated learning: A survey
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2023.3238823
– start-page: 1
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b15
  article-title: A survey on federated learning systems: Vision, hype and reality for data privacy and protection
  publication-title: IEEE Trans. Knowl. Data Eng.
– ident: 10.1016/j.patcog.2024.110424_b148
  doi: 10.1109/CVPR46437.2021.00107
– year: 2019
  ident: 10.1016/j.patcog.2024.110424_b157
– start-page: 367
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b60
  article-title: Federated contrastive learning for volumetric medical image segmentation
– volume: 26
  start-page: 5596
  issue: 11
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b39
  article-title: Customized federated learning for multi-source decentralized medical image classification
  publication-title: IEEE J. Biomed. Health Inf.
  doi: 10.1109/JBHI.2022.3198440
– start-page: 111
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b95
  article-title: Pysyft: A library for easy federated learning
– volume: 41
  start-page: 3663
  issue: 12
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b82
  article-title: Personalized retrogress-resilient federated learning toward imbalanced medical data
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2022.3192483
– volume: 28
  year: 2015
  ident: 10.1016/j.patcog.2024.110424_b164
  article-title: Semi-supervised factored logistic regression for high-dimensional neuroimaging data
  publication-title: Adv. Neural Inf. Process. Syst.
– volume: 55
  start-page: 1
  issue: 11
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b181
  article-title: Blockchain-empowered federated learning: Challenges, solutions, and future directions
  publication-title: ACM Comput. Surv.
  doi: 10.1145/3570953
– ident: 10.1016/j.patcog.2024.110424_b107
  doi: 10.1609/aaai.v33i01.3301590
– start-page: 168
  year: 2018
  ident: 10.1016/j.patcog.2024.110424_b143
  article-title: Skin lesion analysis toward melanoma detection: A challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC)
– volume: 42
  issue: 7
  year: 2023
  ident: 10.1016/j.patcog.2024.110424_b180
  article-title: A dataset auditing method for collaboratively trained machine learning models
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2022.3220706
– volume: 72
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b175
  article-title: Autoencoder based self-supervised test-time adaptation for medical image analysis
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2021.102136
– volume: 19
  start-page: 1523
  issue: 11
  year: 2016
  ident: 10.1016/j.patcog.2024.110424_b106
  article-title: Multimodal population brain imaging in the UK Biobank prospective epidemiological study
  publication-title: Nature Neurosci.
  doi: 10.1038/nn.4393
– year: 2019
  ident: 10.1016/j.patcog.2024.110424_b139
– volume: Vol. 10
  start-page: 1
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b136
  article-title: BUSIS: A benchmark for breast ultrasound image segmentation
– start-page: 1
  year: 2011
  ident: 10.1016/j.patcog.2024.110424_b146
  article-title: RIM-ONE: An open retinal image database for optic nerve evaluation
– year: 2023
  ident: 10.1016/j.patcog.2024.110424_b145
  article-title: Federated semi-supervised learning for medical image segmentation via pseudo-label denoising
  publication-title: IEEE J. Biomed. Health Inf.
  doi: 10.1109/JBHI.2023.3274498
– start-page: 357
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b156
  article-title: Federated whole prostate segmentation in MRI with personalized neural architectures
– volume: 521
  start-page: 436
  issue: 7553
  year: 2015
  ident: 10.1016/j.patcog.2024.110424_b5
  article-title: Deep learning
  publication-title: Nature
  doi: 10.1038/nature14539
– volume: 40
  start-page: 2306
  issue: 9
  year: 2021
  ident: 10.1016/j.patcog.2024.110424_b118
  article-title: Results of the 2020 fastMRI challenge for machine learning MR image reconstruction
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2021.3075856
– volume: 32
  start-page: 1
  year: 2020
  ident: 10.1016/j.patcog.2024.110424_b141
  article-title: PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
  publication-title: Data Brief
  doi: 10.1016/j.dib.2020.106221
– ident: 10.1016/j.patcog.2024.110424_b58
  doi: 10.1109/CVPR42600.2020.00674
– volume: 3
  start-page: 172
  year: 2022
  ident: 10.1016/j.patcog.2024.110424_b172
  article-title: Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge
  publication-title: IEEE Open J. Comput. Soc.
  doi: 10.1109/OJCS.2022.3206407
– year: 2019
  ident: 10.1016/j.patcog.2024.110424_b184
– volume: 139
  start-page: 470
  year: 2016
  ident: 10.1016/j.patcog.2024.110424_b165
  article-title: Domain adaptation for Alzheimer’s disease diagnostics
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2016.05.053
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Snippet Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain...
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SubjectTerms Data privacy
Federated learning
Machine learning
Medical image analysis
Title Federated learning for medical image analysis: A survey
URI https://dx.doi.org/10.1016/j.patcog.2024.110424
https://www.ncbi.nlm.nih.gov/pubmed/38559674
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