High-accuracy and high-throughput reactive lymphocyte identification using lightweight neural networks
•We propose an automatic method to identify reactive lymphocytes and enhance the diagnosis rate of infectious diseases.•We combine CNN and Transformer to extract complex cell features precisely.•We employ knowledge distillation to enhance the model’s discriminative ability.•We adopt a new peripheral...
Saved in:
Published in | Biomedical signal processing and control Vol. 97; p. 106722 |
---|---|
Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.11.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | •We propose an automatic method to identify reactive lymphocytes and enhance the diagnosis rate of infectious diseases.•We combine CNN and Transformer to extract complex cell features precisely.•We employ knowledge distillation to enhance the model’s discriminative ability.•We adopt a new peripheral blood cell dataset to assist in the diagnosis of infectious diseases.
Infectious diseases caused by viruses are generally self-limited diseases with good prognoses, but their clinical manifestations and laboratory test results have a wide range of diversity. However, current clinical examination methods mainly focus on tumor diseases, which may lead to misdiagnosis and missed diagnosis of infectious diseases. Recognizing that the elevation of reactive lymphocytes is a significant indicator for diagnosing infectious diseases and considering the clinical necessity of integrating different cell proportion changes as a foundation for treatment, we have developed an effective and lightweight multi-classification model for diagnosing infectious diseases. Due to the diversity and difficulty of distinguishing reactive lymphocytes, we combine the efficiency of convolutional neural networks to model local features with the capability of the transformer to dynamically model remote interactions, which can extract complex features of cells while performing efficient processing operations. In addition, to further enhance the model’s recognition capabilities, we employ knowledge distillation techniques for data augmentation. According to our results, our model can identify six types of cell lines including reactive lymphocytes in peripheral blood with an average accuracy of 93.55% and can process more than 1300 cells/s per second, which greatly reduces labor costs. Providing early diagnosis of diseases and interventions for patients has significant practical implications. Artificial intelligence-based methods for morphometry of peripheral blood cells are further developed in this study, with potential clinical applications. |
---|---|
AbstractList | •We propose an automatic method to identify reactive lymphocytes and enhance the diagnosis rate of infectious diseases.•We combine CNN and Transformer to extract complex cell features precisely.•We employ knowledge distillation to enhance the model’s discriminative ability.•We adopt a new peripheral blood cell dataset to assist in the diagnosis of infectious diseases.
Infectious diseases caused by viruses are generally self-limited diseases with good prognoses, but their clinical manifestations and laboratory test results have a wide range of diversity. However, current clinical examination methods mainly focus on tumor diseases, which may lead to misdiagnosis and missed diagnosis of infectious diseases. Recognizing that the elevation of reactive lymphocytes is a significant indicator for diagnosing infectious diseases and considering the clinical necessity of integrating different cell proportion changes as a foundation for treatment, we have developed an effective and lightweight multi-classification model for diagnosing infectious diseases. Due to the diversity and difficulty of distinguishing reactive lymphocytes, we combine the efficiency of convolutional neural networks to model local features with the capability of the transformer to dynamically model remote interactions, which can extract complex features of cells while performing efficient processing operations. In addition, to further enhance the model’s recognition capabilities, we employ knowledge distillation techniques for data augmentation. According to our results, our model can identify six types of cell lines including reactive lymphocytes in peripheral blood with an average accuracy of 93.55% and can process more than 1300 cells/s per second, which greatly reduces labor costs. Providing early diagnosis of diseases and interventions for patients has significant practical implications. Artificial intelligence-based methods for morphometry of peripheral blood cells are further developed in this study, with potential clinical applications. |
ArticleNumber | 106722 |
Author | Xu, Chuan Lei, Cheng Xiong, Bei Peng, Haorang Zha, Wenqi Mei, Liye Jin, Shuangtong Shen, Hui Huang, Tingting He, Jing Zhang, Songsong Yang, Wei |
Author_xml | – sequence: 1 givenname: Liye surname: Mei fullname: Mei, Liye organization: School of Computer Science, Hubei University of Technology, Wuhan 430068, China – sequence: 2 givenname: Shuangtong surname: Jin fullname: Jin, Shuangtong organization: School of Computer Science, Hubei University of Technology, Wuhan 430068, China – sequence: 3 givenname: Tingting surname: Huang fullname: Huang, Tingting organization: The Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China – sequence: 4 givenname: Haorang surname: Peng fullname: Peng, Haorang organization: School of Computer Science, Hubei University of Technology, Wuhan 430068, China – sequence: 5 givenname: Wenqi surname: Zha fullname: Zha, Wenqi organization: School of Pharmacy, Jining Medical University, Rizhao 276826, China – sequence: 6 givenname: Jing surname: He fullname: He, Jing organization: The Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China – sequence: 7 givenname: Songsong surname: Zhang fullname: Zhang, Songsong organization: School of Computer Science, Hubei University of Technology, Wuhan 430068, China – sequence: 8 givenname: Chuan surname: Xu fullname: Xu, Chuan organization: School of Computer Science, Hubei University of Technology, Wuhan 430068, China – sequence: 9 givenname: Wei surname: Yang fullname: Yang, Wei organization: School of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China – sequence: 10 givenname: Hui surname: Shen fullname: Shen, Hui email: shenhui@znhospital.cn organization: The Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China – sequence: 11 givenname: Cheng surname: Lei fullname: Lei, Cheng organization: The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China – sequence: 12 givenname: Bei surname: Xiong fullname: Xiong, Bei email: xiongbei909@aliyun.com organization: The Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China |
BookMark | eNp9kMtqwzAQRbVIoUnaH-hKP-BUshVbhm5K6AsC3bRroYxGsVJHNpKckL-vTbruZi5cOJfhLMjMdx4JeeBsxRkvHw-rXexhlbNcjEVZ5fmMzHklykyyWtySRYwHxoSsuJgT--72TaYBhqDhQrU3tJma1IRu2Df9kGhADcmdkLaXY990cElInUGfnHWgk-s8HaLze9qOYDrjdKnHcbAdI5278BPvyI3VbcT7v1yS79eXr817tv18-9g8bzPgkqeMC2tKKHAtjWRMCl4bbgCkAcOtLQvNkRtRcynR1qKyhVzvGKwLU-lCwM4WS5JfdyF0MQa0qg_uqMNFcaYmO-qgJjtqsqOudkbo6Qrh-NnJYVARHHpA4wJCUqZz_-G_5Kp1bg |
Cites_doi | 10.3390/s22155520 10.1038/s42256-019-0101-9 10.1016/j.compmedimag.2020.101699 10.1016/j.irbm.2020.08.005 10.1111/ijlh.12831 10.1111/ijlh.12667 10.1186/s12938-015-0037-1 10.4103/2153-3539.93895 10.1016/j.engappai.2018.04.024 10.1162/neco_a_00990 10.1109/RADIOELEKTRONIKA54537.2022.9764909 10.1016/j.cmpb.2019.105020 10.1016/j.cmpb.2017.11.015 10.3748/wjg.v26.i33.4983 10.1136/jclinpath-2019-205949 10.1038/s41579-021-00639-z 10.1016/j.cmpb.2020.105913 10.3390/electronics12020322 10.1016/j.media.2021.102099 10.1111/j.1751-553X.2008.01100.x 10.1109/ICCV.2017.74 10.1109/ICCV51070.2023.00134 10.1504/IJCSYSE.2018.091407 10.1155/2019/7519603 10.1364/BOE.475166 10.1111/bjh.18480 10.24132/CSRN.2021.3002.8 10.1002/ajh.25785 10.22266/ijies2022.1031.54 10.1097/MD.0000000000023154 10.1109/TMI.2023.3248559 10.1309/AJCP78IFSTOGZZJN 10.1177/0300060520924550 10.1309/AJCP7D8ECZRXGWCG 10.3390/e24040522 10.1016/j.immuni.2020.05.002 10.1038/nature14539 10.1109/WIECON-ECE52138.2020.9397987 10.1016/j.micron.2018.01.010 10.1016/j.bspc.2022.103590 10.3390/computers9020029 |
ContentType | Journal Article |
Copyright | 2024 Elsevier Ltd |
Copyright_xml | – notice: 2024 Elsevier Ltd |
DBID | AAYXX CITATION |
DOI | 10.1016/j.bspc.2024.106722 |
DatabaseName | CrossRef |
DatabaseTitle | CrossRef |
DatabaseTitleList | |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering |
ExternalDocumentID | 10_1016_j_bspc_2024_106722 S1746809424007808 |
GroupedDBID | --- --K --M .~1 0R~ 1B1 1~. 1~5 23N 4.4 457 4G. 5GY 5VS 6J9 7-5 71M 8P~ AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SES SPC SPCBC SST SSV SSZ T5K UNMZH ~G- AAXKI AAYXX AFJKZ CITATION |
ID | FETCH-LOGICAL-c181t-14fd6c3e58d8008419d1dcc8dcd1ff63a1e1d49188ef947f385b0c53d7a34cbf3 |
IEDL.DBID | .~1 |
ISSN | 1746-8094 |
IngestDate | Wed Sep 25 14:11:23 EDT 2024 Sat Aug 31 16:02:35 EDT 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Keywords | Deep learning Reactive lymphocytes Lightweight Convolutional neural network Peripheral blood |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c181t-14fd6c3e58d8008419d1dcc8dcd1ff63a1e1d49188ef947f385b0c53d7a34cbf3 |
ParticipantIDs | crossref_primary_10_1016_j_bspc_2024_106722 elsevier_sciencedirect_doi_10_1016_j_bspc_2024_106722 |
PublicationCentury | 2000 |
PublicationDate | November 2024 2024-11-00 |
PublicationDateYYYYMMDD | 2024-11-01 |
PublicationDate_xml | – month: 11 year: 2024 text: November 2024 |
PublicationDecade | 2020 |
PublicationTitle | Biomedical signal processing and control |
PublicationYear | 2024 |
Publisher | Elsevier Ltd |
Publisher_xml | – name: Elsevier Ltd |
References | Rawat, Wang (b0080) 2017; 29 Matek, Schwarz, Spiekermann, Marr (b0035) 2019; 1 Zheng, Wang, Wang, Liu (b0060) 2018; 107 LeCun, Bengio, Hinton (b0075) 2015; 521 Acevedo, Alférez, Merino, Puigví, Rodellar (b0105) 2019; 180 Fan, Lim, Chong, Chan, Ong, Kuperan (b0030) 2020; 95 Prinyakupt, Pluempitiwiriyawej (b0050) 2015; 14 M.S. Junayed, N. Anjum, A. Noman, B. Islam, A deep CNN model for skin cancer detection and classification, (2021). Boldú, Merino, Alférez, Molina, Acevedo, Rodellar (b0235) 2019; 72 Shahin, Guo, Amin, Sharawi (b0115) 2019; 168 Ramesh, Dangott, Salama, Tasdizen (b0045) 2012; 3 J. Zhang, X. Li, J. Li, L. Liu, Z. Xue, B. Zhang, Z. Jiang, T. Huang, Y. Wang, C. Wang, Rethinking Mobile Block for Efficient Attention-based Models, Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 1389-1400. Xu, Ye, Mei, Shen, Sun, Wang, Yang (b0200) 2023 Baydilli, Atila (b0225) 2020; 80 Atteia, Alhussan, Samee (b0120) 2022; 22 E. Tuba, I. Strumberger, I. Tuba, N. Bacanin, M. Tuba, Acute lymphoblastic leukemia detection by tuned convolutional neural network, 2022 32nd International Conference Radioelektronika (RADIOELEKTRONIKA), IEEE, 2022, pp. 1-4. Loshchilov, Hutter (b0210) 2017 Paszke, Gross, Massa, Lerer, Bradbury, Chanan, Killeen, Lin, Gimelshein, Antiga (b0220) 2019; 32 Gao, Mao, Wu, Li, Zhao, Zhang, Wu, Yu, Xing, Gong (b0155) 2023 Vabret, Britton, Gruber, Hegde, Kim, Kuksin, Levantovsky, Malle, Moreira, Park (b0170) 2020; 52 Leng, Leng, Ge, Dong (b0125) 2022; 75 Huang, Guang, Li, Liu, Zhang, Huang (b0160) 2020; 99 Ansari, Navin, Sangar, Gharamaleki, Danishvar (b0150) 2023; 12 Islam, Sagor, Tuli (b0025) 2020; 21 G. Hinton, O. Vinyals, J. Dean, Distilling the knowledge in a neural network, arXiv preprint arXiv:1503.02531 (2015). Mei, Shen, Yu, Weng, Li, Zahid, Huang, Wang, Liu, Zhou (b0180) 2022; 13 Chabot-Richards, Foucar (b0165) 2017; 39 Debuysschere, Beukinga, Hernando, Blairon, Tré-Hardy, Cupaiolo (b0005) 2022; 199 Rawat, Singh, Bhadauria, Virmani, Devgun (b0110) 2018; 4 Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin (b0195) 2017; 30 Meintker, Ringwald, Rauh, Krause (b0070) 2013; 139 Teerasarntipan, Chaiteerakij, Komolmit, Tangkijvanich, Treeprasertsuk (b0020) 2020; 26 Alférez, Merino, Bigorra, Mujica, Ruiz, Rodellar (b0065) 2015; 143 Pansombut, Wikaisuksakul, Khongkraphan, Phon-On (b0140) 2019; 2019 Tsochatzidis, Koutla, Costaridou, Pratikakis (b0100) 2021; 200 Loey, Naman, Zayed (b0130) 2020; 9 A.R. Revanda, C. Fatichah, N. Suciati, Classification of acute lymphoblastic leukemia on white blood cell microscopy images based on instance segmentation using mask R-CNN, vol 15 (2022) 625-637. Lee, Erber, Porwit, Tomonaga, Peterson, Hematology (b0175) 2008; 30 Patil, Patil, Birajdar (b0230) 2021; 42 Wu, Ma, Zhang, Zu, Gu, Ding, Zhang (b0015) 2020; 48 Gehlot, Gupta, Gupta (b0085) 2021; 72 Baker, Mahmud, Miller, Rajeev, Rasambainarivo, Rice, Takahashi, Tatem, Wagner, Wang (b0010) 2022; 20 Vogado, Veras, Araujo, Silva, Aires (b0055) 2018; 72 N. Rezaoana, M.S. Hossain, K. Andersson, Detection and classification of skin cancer by using a parallel CNN model, 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, 2020, pp. 380-386. R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, Proceedings of the IEEE international conference on computer vision, 2017, pp. 618-626. Mei, Yu, Shen, Weng, Liu, Wang, Liu, Zhou, Lei (b0190) 2022; 24 Merino, Boldú, Ermens (b0040) 2018; 40 Zhang, Cisse, Dauphin, Lopez-Paz (b0215) 2017 Baker (10.1016/j.bspc.2024.106722_b0010) 2022; 20 Shahin (10.1016/j.bspc.2024.106722_b0115) 2019; 168 Vogado (10.1016/j.bspc.2024.106722_b0055) 2018; 72 Mei (10.1016/j.bspc.2024.106722_b0190) 2022; 24 Merino (10.1016/j.bspc.2024.106722_b0040) 2018; 40 Gao (10.1016/j.bspc.2024.106722_b0155) 2023 Wu (10.1016/j.bspc.2024.106722_b0015) 2020; 48 Rawat (10.1016/j.bspc.2024.106722_b0080) 2017; 29 LeCun (10.1016/j.bspc.2024.106722_b0075) 2015; 521 Baydilli (10.1016/j.bspc.2024.106722_b0225) 2020; 80 Loey (10.1016/j.bspc.2024.106722_b0130) 2020; 9 Chabot-Richards (10.1016/j.bspc.2024.106722_b0165) 2017; 39 Patil (10.1016/j.bspc.2024.106722_b0230) 2021; 42 Fan (10.1016/j.bspc.2024.106722_b0030) 2020; 95 Islam (10.1016/j.bspc.2024.106722_b0025) 2020; 21 Vabret (10.1016/j.bspc.2024.106722_b0170) 2020; 52 Vaswani (10.1016/j.bspc.2024.106722_b0195) 2017; 30 Gehlot (10.1016/j.bspc.2024.106722_b0085) 2021; 72 10.1016/j.bspc.2024.106722_b0095 Alférez (10.1016/j.bspc.2024.106722_b0065) 2015; 143 Loshchilov (10.1016/j.bspc.2024.106722_b0210) 2017 Huang (10.1016/j.bspc.2024.106722_b0160) 2020; 99 Matek (10.1016/j.bspc.2024.106722_b0035) 2019; 1 Tsochatzidis (10.1016/j.bspc.2024.106722_b0100) 2021; 200 10.1016/j.bspc.2024.106722_b0135 Zheng (10.1016/j.bspc.2024.106722_b0060) 2018; 107 Rawat (10.1016/j.bspc.2024.106722_b0110) 2018; 4 Atteia (10.1016/j.bspc.2024.106722_b0120) 2022; 22 Lee (10.1016/j.bspc.2024.106722_b0175) 2008; 30 Ramesh (10.1016/j.bspc.2024.106722_b0045) 2012; 3 Meintker (10.1016/j.bspc.2024.106722_b0070) 2013; 139 Xu (10.1016/j.bspc.2024.106722_b0200) 2023 Prinyakupt (10.1016/j.bspc.2024.106722_b0050) 2015; 14 Boldú (10.1016/j.bspc.2024.106722_b0235) 2019; 72 Teerasarntipan (10.1016/j.bspc.2024.106722_b0020) 2020; 26 10.1016/j.bspc.2024.106722_b0090 Pansombut (10.1016/j.bspc.2024.106722_b0140) 2019; 2019 Ansari (10.1016/j.bspc.2024.106722_b0150) 2023; 12 Zhang (10.1016/j.bspc.2024.106722_b0215) 2017 Mei (10.1016/j.bspc.2024.106722_b0180) 2022; 13 10.1016/j.bspc.2024.106722_b0185 10.1016/j.bspc.2024.106722_b0240 Debuysschere (10.1016/j.bspc.2024.106722_b0005) 2022; 199 10.1016/j.bspc.2024.106722_b0205 Leng (10.1016/j.bspc.2024.106722_b0125) 2022; 75 10.1016/j.bspc.2024.106722_b0145 Acevedo (10.1016/j.bspc.2024.106722_b0105) 2019; 180 Paszke (10.1016/j.bspc.2024.106722_b0220) 2019; 32 |
References_xml | – volume: 14 start-page: 1 year: 2015 end-page: 19 ident: b0050 article-title: Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers publication-title: Biomed. Eng. Online contributor: fullname: Pluempitiwiriyawej – volume: 26 start-page: 4983 year: 2020 ident: b0020 article-title: Acute liver failure and death predictors in patients with dengue-induced severe hepatitis publication-title: World J. Gastroenterol. contributor: fullname: Treeprasertsuk – volume: 99 year: 2020 ident: b0160 article-title: AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: a STARD compliant diagnosis research publication-title: Medicine contributor: fullname: Huang – volume: 22 start-page: 5520 year: 2022 ident: b0120 article-title: Bo-allcnn: Bayesian-based optimized cnn for acute lymphoblastic leukemia detection in microscopic blood smear images publication-title: Sensors contributor: fullname: Samee – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: b0075 article-title: Deep Learning publication-title: Nature contributor: fullname: Hinton – volume: 72 start-page: 755 year: 2019 end-page: 761 ident: b0235 article-title: Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis publication-title: J. Clin. Pathol. contributor: fullname: Rodellar – year: 2023 ident: b0200 article-title: Cross-Attention guided group aggregation network for cropland change detection publication-title: IEEE Sens. J. contributor: fullname: Yang – volume: 30 start-page: 349 year: 2008 end-page: 364 ident: b0175 article-title: ICSH guidelines for the standardization of bone marrow specimens and reports publication-title: Int. J. Lab. Hematol. contributor: fullname: Hematology – volume: 2019 year: 2019 ident: b0140 article-title: Convolutional neural networks for recognition of lymphoblast cell images publication-title: Comput. Intell. Neurosci. contributor: fullname: Phon-On – volume: 139 start-page: 641 year: 2013 end-page: 650 ident: b0070 article-title: Comparison of automated differential blood cell counts from Abbott Sapphire, Siemens Advia 120, Beckman Coulter DxH 800, and Sysmex XE-2100 in normal and pathologic samples publication-title: Am. J. Clin. Pathol. contributor: fullname: Krause – volume: 72 year: 2021 ident: b0085 article-title: A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis publication-title: Med. Image Anal. contributor: fullname: Gupta – volume: 3 start-page: 13 year: 2012 ident: b0045 article-title: Isolation and two-step classification of normal white blood cells in peripheral blood smears publication-title: J. Pathol. Infor. contributor: fullname: Tasdizen – volume: 52 start-page: 910 year: 2020 end-page: 941 ident: b0170 article-title: Immunology of COVID-19: current state of the science publication-title: Immunity contributor: fullname: Park – volume: 107 start-page: 55 year: 2018 end-page: 71 ident: b0060 article-title: Fast and robust segmentation of white blood cell images by self-supervised learning publication-title: Micron contributor: fullname: Liu – volume: 29 start-page: 2352 year: 2017 end-page: 2449 ident: b0080 article-title: Deep convolutional neural networks for image classification: a comprehensive review publication-title: Neural Comput. contributor: fullname: Wang – year: 2017 ident: b0210 article-title: Decoupled weight decay regularization publication-title: arXiv preprint arXiv:1711.05101 contributor: fullname: Hutter – volume: 80 year: 2020 ident: b0225 article-title: Classification of white blood cells using capsule networks publication-title: Comput. Med. Imaging Graph. contributor: fullname: Atila – volume: 48 year: 2020 ident: b0015 article-title: Clinical manifestations and laboratory results of 61 children with infectious mononucleosis publication-title: J. Int. Med. Res. contributor: fullname: Zhang – volume: 32 year: 2019 ident: b0220 article-title: Pytorch: an imperative style, high-performance deep learning library publication-title: Adv. Neural Inf. Proces. Syst. contributor: fullname: Antiga – volume: 42 start-page: 378 year: 2021 end-page: 389 ident: b0230 article-title: White blood cells image classification using deep learning with canonical correlation analysis publication-title: Irbm contributor: fullname: Birajdar – year: 2023 ident: b0155 article-title: Childhood leukemia classification via information bottleneck enhanced hierarchical multi-instance learning publication-title: IEEE Trans. Med. Imaging contributor: fullname: Gong – volume: 24 start-page: 522 year: 2022 ident: b0190 article-title: Adversarial multiscale feature learning framework for overlapping chromosome segmentation publication-title: Entropy contributor: fullname: Lei – volume: 1 start-page: 538 year: 2019 end-page: 544 ident: b0035 article-title: Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks publication-title: Nat. Mach. Intell. contributor: fullname: Marr – volume: 39 start-page: 23 year: 2017 end-page: 30 ident: b0165 article-title: Does morphology matter in 2017? an approach to morphologic clues in non-neoplastic blood and bone marrow disorders publication-title: Int. J. Lab. Hematol. contributor: fullname: Foucar – volume: 30 year: 2017 ident: b0195 article-title: Attention is all you need publication-title: Adv. Neural Inf. Proces. Syst. contributor: fullname: Polosukhin – volume: 21 start-page: 123 year: 2020 ident: b0025 article-title: Haemophagocytic lymphohistiocytosis associated with dengue fever-a case series publication-title: J. Med. contributor: fullname: Tuli – volume: 12 start-page: 322 year: 2023 ident: b0150 article-title: A customized efficient deep learning model for the diagnosis of acute leukemia cells based on lymphocyte and monocyte images publication-title: Electronics contributor: fullname: Danishvar – volume: 4 start-page: 202 year: 2018 end-page: 216 ident: b0110 article-title: Application of ensemble artificial neural network for the classification of white blood cells using microscopic blood images publication-title: Int. J. Comput. Syst. Eng. contributor: fullname: Devgun – volume: 143 start-page: 168 year: 2015 end-page: 176 ident: b0065 article-title: Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis publication-title: Am. J. Clin. Pathol. contributor: fullname: Rodellar – volume: 199 start-page: 306 year: 2022 ident: b0005 article-title: Atypical lymphocytes associated with monkeypox virus infection publication-title: Br. J. Haematol. contributor: fullname: Cupaiolo – volume: 168 start-page: 69 year: 2019 end-page: 80 ident: b0115 article-title: White blood cells identification system based on convolutional deep neural learning networks publication-title: Comput. Methods Programs Biomed. contributor: fullname: Sharawi – year: 2017 ident: b0215 article-title: Mixup: Beyond empirical risk minimization publication-title: arXiv preprint arXiv:1710.09412 contributor: fullname: Lopez-Paz – volume: 20 start-page: 193 year: 2022 end-page: 205 ident: b0010 article-title: Infectious disease in an era of global change publication-title: Nat. Rev. Microbiol. contributor: fullname: Wang – volume: 75 year: 2022 ident: b0125 article-title: Knowledge distillation-based deep learning classification network for peripheral blood leukocytes publication-title: Biomed. Signal Process. Control contributor: fullname: Dong – volume: 72 start-page: 415 year: 2018 end-page: 422 ident: b0055 article-title: Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification publication-title: Eng. Appl. Artif. Intel. contributor: fullname: Aires – volume: 13 start-page: 6631 year: 2022 end-page: 6644 ident: b0180 article-title: High-throughput and high-accuracy diagnosis of multiple myeloma with multi-object detection publication-title: Biomed. Opt. Express contributor: fullname: Zhou – volume: 40 start-page: 109 year: 2018 end-page: 119 ident: b0040 article-title: Acute myeloid leukaemia: how to combine multiple tools publication-title: Int. J. Lab. Hematol. contributor: fullname: Ermens – volume: 9 start-page: 29 year: 2020 ident: b0130 article-title: Deep transfer learning in diagnosing leukemia in blood cells publication-title: Computers contributor: fullname: Zayed – volume: 95 start-page: 723 year: 2020 ident: b0030 article-title: COVID-19 and mycoplasma pneumoniae coinfection publication-title: Am. J. Hematol. contributor: fullname: Kuperan – volume: 200 year: 2021 ident: b0100 article-title: Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses publication-title: Comput. Methods Programs Biomed. contributor: fullname: Pratikakis – volume: 180 year: 2019 ident: b0105 article-title: Recognition of peripheral blood cell images using convolutional neural networks publication-title: Comput. Methods Programs Biomed. contributor: fullname: Rodellar – volume: 22 start-page: 5520 issue: 15 year: 2022 ident: 10.1016/j.bspc.2024.106722_b0120 article-title: Bo-allcnn: Bayesian-based optimized cnn for acute lymphoblastic leukemia detection in microscopic blood smear images publication-title: Sensors doi: 10.3390/s22155520 contributor: fullname: Atteia – volume: 1 start-page: 538 issue: 11 year: 2019 ident: 10.1016/j.bspc.2024.106722_b0035 article-title: Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks publication-title: Nat. Mach. Intell. doi: 10.1038/s42256-019-0101-9 contributor: fullname: Matek – volume: 80 year: 2020 ident: 10.1016/j.bspc.2024.106722_b0225 article-title: Classification of white blood cells using capsule networks publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2020.101699 contributor: fullname: Baydilli – volume: 42 start-page: 378 issue: 5 year: 2021 ident: 10.1016/j.bspc.2024.106722_b0230 article-title: White blood cells image classification using deep learning with canonical correlation analysis publication-title: Irbm doi: 10.1016/j.irbm.2020.08.005 contributor: fullname: Patil – volume: 40 start-page: 109 year: 2018 ident: 10.1016/j.bspc.2024.106722_b0040 article-title: Acute myeloid leukaemia: how to combine multiple tools publication-title: Int. J. Lab. Hematol. doi: 10.1111/ijlh.12831 contributor: fullname: Merino – volume: 32 year: 2019 ident: 10.1016/j.bspc.2024.106722_b0220 article-title: Pytorch: an imperative style, high-performance deep learning library publication-title: Adv. Neural Inf. Proces. Syst. contributor: fullname: Paszke – volume: 39 start-page: 23 year: 2017 ident: 10.1016/j.bspc.2024.106722_b0165 article-title: Does morphology matter in 2017? an approach to morphologic clues in non-neoplastic blood and bone marrow disorders publication-title: Int. J. Lab. Hematol. doi: 10.1111/ijlh.12667 contributor: fullname: Chabot-Richards – volume: 14 start-page: 1 year: 2015 ident: 10.1016/j.bspc.2024.106722_b0050 article-title: Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers publication-title: Biomed. Eng. Online doi: 10.1186/s12938-015-0037-1 contributor: fullname: Prinyakupt – volume: 3 start-page: 13 issue: 1 year: 2012 ident: 10.1016/j.bspc.2024.106722_b0045 article-title: Isolation and two-step classification of normal white blood cells in peripheral blood smears publication-title: J. Pathol. Infor. doi: 10.4103/2153-3539.93895 contributor: fullname: Ramesh – volume: 72 start-page: 415 year: 2018 ident: 10.1016/j.bspc.2024.106722_b0055 article-title: Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification publication-title: Eng. Appl. Artif. Intel. doi: 10.1016/j.engappai.2018.04.024 contributor: fullname: Vogado – volume: 29 start-page: 2352 issue: 9 year: 2017 ident: 10.1016/j.bspc.2024.106722_b0080 article-title: Deep convolutional neural networks for image classification: a comprehensive review publication-title: Neural Comput. doi: 10.1162/neco_a_00990 contributor: fullname: Rawat – ident: 10.1016/j.bspc.2024.106722_b0135 doi: 10.1109/RADIOELEKTRONIKA54537.2022.9764909 – volume: 180 year: 2019 ident: 10.1016/j.bspc.2024.106722_b0105 article-title: Recognition of peripheral blood cell images using convolutional neural networks publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2019.105020 contributor: fullname: Acevedo – volume: 168 start-page: 69 year: 2019 ident: 10.1016/j.bspc.2024.106722_b0115 article-title: White blood cells identification system based on convolutional deep neural learning networks publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2017.11.015 contributor: fullname: Shahin – volume: 26 start-page: 4983 issue: 33 year: 2020 ident: 10.1016/j.bspc.2024.106722_b0020 article-title: Acute liver failure and death predictors in patients with dengue-induced severe hepatitis publication-title: World J. Gastroenterol. doi: 10.3748/wjg.v26.i33.4983 contributor: fullname: Teerasarntipan – volume: 72 start-page: 755 issue: 11 year: 2019 ident: 10.1016/j.bspc.2024.106722_b0235 article-title: Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis publication-title: J. Clin. Pathol. doi: 10.1136/jclinpath-2019-205949 contributor: fullname: Boldú – volume: 20 start-page: 193 issue: 4 year: 2022 ident: 10.1016/j.bspc.2024.106722_b0010 article-title: Infectious disease in an era of global change publication-title: Nat. Rev. Microbiol. doi: 10.1038/s41579-021-00639-z contributor: fullname: Baker – volume: 200 year: 2021 ident: 10.1016/j.bspc.2024.106722_b0100 article-title: Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2020.105913 contributor: fullname: Tsochatzidis – volume: 12 start-page: 322 issue: 2 year: 2023 ident: 10.1016/j.bspc.2024.106722_b0150 article-title: A customized efficient deep learning model for the diagnosis of acute leukemia cells based on lymphocyte and monocyte images publication-title: Electronics doi: 10.3390/electronics12020322 contributor: fullname: Ansari – volume: 30 year: 2017 ident: 10.1016/j.bspc.2024.106722_b0195 article-title: Attention is all you need publication-title: Adv. Neural Inf. Proces. Syst. contributor: fullname: Vaswani – volume: 72 year: 2021 ident: 10.1016/j.bspc.2024.106722_b0085 article-title: A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis publication-title: Med. Image Anal. doi: 10.1016/j.media.2021.102099 contributor: fullname: Gehlot – volume: 30 start-page: 349 issue: 5 year: 2008 ident: 10.1016/j.bspc.2024.106722_b0175 article-title: ICSH guidelines for the standardization of bone marrow specimens and reports publication-title: Int. J. Lab. Hematol. doi: 10.1111/j.1751-553X.2008.01100.x contributor: fullname: Lee – ident: 10.1016/j.bspc.2024.106722_b0240 doi: 10.1109/ICCV.2017.74 – ident: 10.1016/j.bspc.2024.106722_b0185 doi: 10.1109/ICCV51070.2023.00134 – volume: 4 start-page: 202 issue: 2–3 year: 2018 ident: 10.1016/j.bspc.2024.106722_b0110 article-title: Application of ensemble artificial neural network for the classification of white blood cells using microscopic blood images publication-title: Int. J. Comput. Syst. Eng. doi: 10.1504/IJCSYSE.2018.091407 contributor: fullname: Rawat – volume: 2019 year: 2019 ident: 10.1016/j.bspc.2024.106722_b0140 article-title: Convolutional neural networks for recognition of lymphoblast cell images publication-title: Comput. Intell. Neurosci. doi: 10.1155/2019/7519603 contributor: fullname: Pansombut – volume: 13 start-page: 6631 issue: 12 year: 2022 ident: 10.1016/j.bspc.2024.106722_b0180 article-title: High-throughput and high-accuracy diagnosis of multiple myeloma with multi-object detection publication-title: Biomed. Opt. Express doi: 10.1364/BOE.475166 contributor: fullname: Mei – ident: 10.1016/j.bspc.2024.106722_b0205 – volume: 199 start-page: 306 issue: 3 year: 2022 ident: 10.1016/j.bspc.2024.106722_b0005 article-title: Atypical lymphocytes associated with monkeypox virus infection publication-title: Br. J. Haematol. doi: 10.1111/bjh.18480 contributor: fullname: Debuysschere – ident: 10.1016/j.bspc.2024.106722_b0090 doi: 10.24132/CSRN.2021.3002.8 – volume: 95 start-page: 723 issue: 6 year: 2020 ident: 10.1016/j.bspc.2024.106722_b0030 article-title: COVID-19 and mycoplasma pneumoniae coinfection publication-title: Am. J. Hematol. doi: 10.1002/ajh.25785 contributor: fullname: Fan – ident: 10.1016/j.bspc.2024.106722_b0145 doi: 10.22266/ijies2022.1031.54 – year: 2023 ident: 10.1016/j.bspc.2024.106722_b0200 article-title: Cross-Attention guided group aggregation network for cropland change detection publication-title: IEEE Sens. J. contributor: fullname: Xu – volume: 99 issue: 45 year: 2020 ident: 10.1016/j.bspc.2024.106722_b0160 article-title: AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: a STARD compliant diagnosis research publication-title: Medicine doi: 10.1097/MD.0000000000023154 contributor: fullname: Huang – year: 2023 ident: 10.1016/j.bspc.2024.106722_b0155 article-title: Childhood leukemia classification via information bottleneck enhanced hierarchical multi-instance learning publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2023.3248559 contributor: fullname: Gao – volume: 143 start-page: 168 issue: 2 year: 2015 ident: 10.1016/j.bspc.2024.106722_b0065 article-title: Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis publication-title: Am. J. Clin. Pathol. doi: 10.1309/AJCP78IFSTOGZZJN contributor: fullname: Alférez – volume: 48 issue: 10 year: 2020 ident: 10.1016/j.bspc.2024.106722_b0015 article-title: Clinical manifestations and laboratory results of 61 children with infectious mononucleosis publication-title: J. Int. Med. Res. doi: 10.1177/0300060520924550 contributor: fullname: Wu – volume: 139 start-page: 641 issue: 5 year: 2013 ident: 10.1016/j.bspc.2024.106722_b0070 article-title: Comparison of automated differential blood cell counts from Abbott Sapphire, Siemens Advia 120, Beckman Coulter DxH 800, and Sysmex XE-2100 in normal and pathologic samples publication-title: Am. J. Clin. Pathol. doi: 10.1309/AJCP7D8ECZRXGWCG contributor: fullname: Meintker – volume: 24 start-page: 522 issue: 4 year: 2022 ident: 10.1016/j.bspc.2024.106722_b0190 article-title: Adversarial multiscale feature learning framework for overlapping chromosome segmentation publication-title: Entropy doi: 10.3390/e24040522 contributor: fullname: Mei – year: 2017 ident: 10.1016/j.bspc.2024.106722_b0215 article-title: Mixup: Beyond empirical risk minimization publication-title: arXiv preprint arXiv:1710.09412 contributor: fullname: Zhang – volume: 52 start-page: 910 issue: 6 year: 2020 ident: 10.1016/j.bspc.2024.106722_b0170 article-title: Immunology of COVID-19: current state of the science publication-title: Immunity doi: 10.1016/j.immuni.2020.05.002 contributor: fullname: Vabret – volume: 21 start-page: 123 issue: 2 year: 2020 ident: 10.1016/j.bspc.2024.106722_b0025 article-title: Haemophagocytic lymphohistiocytosis associated with dengue fever-a case series publication-title: J. Med. contributor: fullname: Islam – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.bspc.2024.106722_b0075 article-title: Deep Learning publication-title: Nature doi: 10.1038/nature14539 contributor: fullname: LeCun – ident: 10.1016/j.bspc.2024.106722_b0095 doi: 10.1109/WIECON-ECE52138.2020.9397987 – volume: 107 start-page: 55 year: 2018 ident: 10.1016/j.bspc.2024.106722_b0060 article-title: Fast and robust segmentation of white blood cell images by self-supervised learning publication-title: Micron doi: 10.1016/j.micron.2018.01.010 contributor: fullname: Zheng – volume: 75 year: 2022 ident: 10.1016/j.bspc.2024.106722_b0125 article-title: Knowledge distillation-based deep learning classification network for peripheral blood leukocytes publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2022.103590 contributor: fullname: Leng – volume: 9 start-page: 29 issue: 2 year: 2020 ident: 10.1016/j.bspc.2024.106722_b0130 article-title: Deep transfer learning in diagnosing leukemia in blood cells publication-title: Computers doi: 10.3390/computers9020029 contributor: fullname: Loey – year: 2017 ident: 10.1016/j.bspc.2024.106722_b0210 article-title: Decoupled weight decay regularization publication-title: arXiv preprint arXiv:1711.05101 contributor: fullname: Loshchilov |
SSID | ssj0048714 |
Score | 2.4021792 |
Snippet | •We propose an automatic method to identify reactive lymphocytes and enhance the diagnosis rate of infectious diseases.•We combine CNN and Transformer to... |
SourceID | crossref elsevier |
SourceType | Aggregation Database Publisher |
StartPage | 106722 |
SubjectTerms | Convolutional neural network Deep learning Lightweight Peripheral blood Reactive lymphocytes |
Title | High-accuracy and high-throughput reactive lymphocyte identification using lightweight neural networks |
URI | https://dx.doi.org/10.1016/j.bspc.2024.106722 |
Volume | 97 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3PS8MwFA5jXvQg_sSfIwdvkq1t0jY7juGYCrvoYLfSviQyGV3ZOmQX_3bz2lQmiAePLQktX9L3vqTf-0LIXcgh0JlnWKAk4NYNfnPKYx4EIo1BeSat1BaTaDwVT7Nw1iLDphYGZZUu9tcxvYrW7k7Podkr5vPei-XSkbSrE1RBxrIq-BU2Gdk53f38lnlYPl75e2Njhq1d4Uyt8crWBdoYBqKLTmpB8Hty2kk4oyNy6JgiHdQvc0xaOj8hBzv-gafEoEqDpQCbVQpbmuaKov0wc4fvFJuSWk5YRTS62NpxW8K21HSunESoGhWK0vc3usBV-ke1UUrR5NI-Oq8l4uszMh09vA7HzB2cwMAm7JL5wqgIuA6lkmiY7_eVrwCkAuUbE_HU174SfV9KbfoiNlyGmQchV3HKBWSGn5N2vsz1BaGW3ykRC0-aSAueChnxSBhtBPgR_qS7JPcNYklR-2MkjXDsPUF8E8Q3qfG9JGEDavJjlBMbwP_od_XPftdkH6_q2sEb0i5XG31rSUSZdapZ0iF7g8fn8eQLdOXIfA |
link.rule.ids | 315,786,790,4521,24144,27957,27958,45620,45714 |
linkProvider | Elsevier |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Na4NAEF1Ccmh7KP2k6eceeis2rrvq5hhCg2nSXJpAbqL7UVKCkcRQ8u-7oyukUHroVR2UNzr7dn3zFqFHnwpPpa52PMkFLN3ANyddxxUeS0IhXZ2UaotJEM3Y69yfN1C_7oUBWaWt_VVNL6u1PdKxaHbyxaLzbrh0wM3sBFSQIYeG3xbzQ8KaqNUbjqJJXZANJS8tvuF6BwJs70wl80o3OTgZeuwZzNQ87_fxaW_MGZygY0sWca96nlPUUNkZOtqzEDxHGoQaTiLEdp2IHU4yicGB2LH77-TbAhtaWBY1vNyZ1K3ErlB4Ia1KqEwMBvX7B17CRP2rXCvF4HNpbp1VKvHNBZoNXqb9yLF7JzjCjNmFQ5iWgaDK55KDZz7pSiKF4FJIonVAE6KIZF3CudJdFmrK_dQVPpVhQplINb1EzWyVqSuEDcWTLGQu14FiNGE8oAHTSjNBAvhP10ZPNWJxXllkxLV27DMGfGPAN67wbSO_BjX-kejY1PA_4q7_GfeADqLp2zgeDyejG3QIZ6pWwlvULNZbdWc4RZHe23fmG29ayzI |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=High-accuracy+and+high-throughput+reactive+lymphocyte+identification+using+lightweight+neural+networks&rft.jtitle=Biomedical+signal+processing+and+control&rft.au=Mei%2C+Liye&rft.au=Jin%2C+Shuangtong&rft.au=Huang%2C+Tingting&rft.au=Peng%2C+Haorang&rft.date=2024-11-01&rft.issn=1746-8094&rft.volume=97&rft.spage=106722&rft_id=info:doi/10.1016%2Fj.bspc.2024.106722&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_bspc_2024_106722 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1746-8094&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1746-8094&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1746-8094&client=summon |