Multi-modal data Alzheimer’s disease detection based on 3D convolution
Multi-modal medical imaging information has been widely used in computer-assisted investigations and diagnoses. A typical example is that the combination of information from multi-modal medical images allows for a more accurate and comprehensive classification and diagnosis of the same Alzheimer’s d...
Saved in:
| Published in | Biomedical signal processing and control Vol. 75; p. 103565 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.05.2022
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Multi-modal medical imaging information has been widely used in computer-assisted investigations and diagnoses. A typical example is that the combination of information from multi-modal medical images allows for a more accurate and comprehensive classification and diagnosis of the same Alzheimer’s disease (AD) subject. This paper proposes an image fusion method to fuse Magnetic Resonance Images (MRI) with Positron Emission Tomography (PET) images from AD patients. In addition, we use 3D convolutional neural networks to evaluate the effectiveness of our image fusion approach in both dichotomous and multi-classification tasks. The 3D convolution of the fused images is used to extract the information from the features, resulting in a richer multi-modal feature information. Finally, the extracted multi-modal traits are classified and predicted using a fully connected neural network. The experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset show that the proposed model achieves better results in terms of accuracy, sensitivity and specificity. |
|---|---|
| AbstractList | Multi-modal medical imaging information has been widely used in computer-assisted investigations and diagnoses. A typical example is that the combination of information from multi-modal medical images allows for a more accurate and comprehensive classification and diagnosis of the same Alzheimer’s disease (AD) subject. This paper proposes an image fusion method to fuse Magnetic Resonance Images (MRI) with Positron Emission Tomography (PET) images from AD patients. In addition, we use 3D convolutional neural networks to evaluate the effectiveness of our image fusion approach in both dichotomous and multi-classification tasks. The 3D convolution of the fused images is used to extract the information from the features, resulting in a richer multi-modal feature information. Finally, the extracted multi-modal traits are classified and predicted using a fully connected neural network. The experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset show that the proposed model achieves better results in terms of accuracy, sensitivity and specificity. |
| ArticleNumber | 103565 |
| Author | Kong, Zhaokai Zhu, Wenjun Zhang, Mengyi Yi, Yang Zhang, Baochang Wang, Tian |
| Author_xml | – sequence: 1 givenname: Zhaokai surname: Kong fullname: Kong, Zhaokai organization: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 210000, Jiangsu, China – sequence: 2 givenname: Mengyi surname: Zhang fullname: Zhang, Mengyi email: myzhang@njtech.edu.cn organization: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 210000, Jiangsu, China – sequence: 3 givenname: Wenjun surname: Zhu fullname: Zhu, Wenjun organization: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 210000, Jiangsu, China – sequence: 4 givenname: Yang surname: Yi fullname: Yi, Yang organization: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 210000, Jiangsu, China – sequence: 5 givenname: Tian surname: Wang fullname: Wang, Tian organization: Institute of Artificial Intelligence, Beihang University, Beijing 100000, Beijing, China – sequence: 6 givenname: Baochang surname: Zhang fullname: Zhang, Baochang organization: Institute of Artificial Intelligence, Beihang University, Beijing 100000, Beijing, China |
| BookMark | eNp9kEtOwzAQQC1UJErhAqx8gRR_YieV2FTlU6QiNrC2HHssXKVxZaeVYMU1uB4nwVFhw6Kr-emNZt45GnWhA4SuKJlSQuX1etqkrZkywlhucCHFCRrTqpRFTUk9-svJrDxD5ymtCSnripZjtHzatb0vNsHqFlvdazxvP97AbyB-f34lbH0CnQBb6MH0PnS4yaXFOeG32IRuH9rd0L9Ap063CS5_4wS93t-9LJbF6vnhcTFfFYZx0hfWNqQGJ5hwptQzxyUH3UhpGuaEdZZaS00jKbDKUmcgjyuuDZeiEdI5wieIHfaaGFKK4NQ2-o2O74oSNbhQazW4UIMLdXCRofofZHyvh7P7qH17HL05oJCf2nuIKhkPnQHrYzaibPDH8B-KqH7l |
| CitedBy_id | crossref_primary_10_3390_make6010024 crossref_primary_10_3389_fbioe_2023_1326706 crossref_primary_10_3390_math11040957 crossref_primary_10_1016_j_compbiomed_2024_109438 crossref_primary_10_1002_jmri_29734 crossref_primary_10_1016_j_bspc_2022_104032 crossref_primary_10_1016_j_bspc_2024_106212 crossref_primary_10_1016_j_compeleceng_2024_109796 crossref_primary_10_1038_s41598_024_56001_9 crossref_primary_10_3390_bioengineering12010073 crossref_primary_10_1007_s00521_024_10399_5 crossref_primary_10_1016_j_eswa_2024_124780 crossref_primary_10_1016_j_heliyon_2024_e34402 crossref_primary_10_3233_IDT_230479 crossref_primary_10_1515_revneuro_2022_0122 crossref_primary_10_1177_08953996241300023 crossref_primary_10_1016_j_aej_2023_09_050 crossref_primary_10_4018_IJSWIS_313715 crossref_primary_10_3390_electronics11233893 crossref_primary_10_1016_j_compbiomed_2024_108635 crossref_primary_10_1007_s11831_024_10179_3 crossref_primary_10_1007_s11042_024_19425_z crossref_primary_10_1002_ima_22967 crossref_primary_10_1038_s41598_024_81563_z crossref_primary_10_1109_TCDS_2023_3254209 crossref_primary_10_1016_j_engappai_2023_107032 crossref_primary_10_1016_j_compbiomed_2023_107050 crossref_primary_10_1002_hsr2_70025 crossref_primary_10_1016_j_bspc_2024_107085 crossref_primary_10_1142_S0218001423560256 crossref_primary_10_1016_j_dsp_2023_104229 crossref_primary_10_1007_s11042_022_13935_4 crossref_primary_10_3390_s22114250 crossref_primary_10_1109_ACCESS_2024_3483833 crossref_primary_10_1007_s10334_024_01178_3 crossref_primary_10_1016_j_cmpb_2025_108703 crossref_primary_10_1007_s12553_024_00823_0 crossref_primary_10_1109_JBHI_2024_3383885 crossref_primary_10_35234_fumbd_1313523 crossref_primary_10_1016_j_eswa_2024_124241 crossref_primary_10_1016_j_cagd_2024_102364 crossref_primary_10_1007_s43538_023_00198_2 crossref_primary_10_1038_s41598_023_33055_9 crossref_primary_10_1007_s10462_023_10644_8 crossref_primary_10_1016_j_inffus_2025_103010 crossref_primary_10_1016_j_bspc_2023_105652 crossref_primary_10_1007_s12539_024_00625_y crossref_primary_10_1016_j_jksuci_2023_101618 crossref_primary_10_3390_jimaging10060141 crossref_primary_10_1109_TNSRE_2025_3549730 |
| Cites_doi | 10.1002/jmri.21049 10.1016/j.neuroimage.2011.12.071 10.1016/j.pscychresns.2019.01.014 10.1016/j.neuroimage.2010.07.033 10.1109/TBME.2014.2372011 10.3389/fninf.2018.00035 10.1016/j.cviu.2017.04.002 10.1007/s11042-016-3482-3 10.1016/j.patcog.2016.10.009 10.1002/cpe.6237 10.1016/j.cger.2017.02.005 10.1016/j.neuroimage.2015.01.048 10.1016/j.bspc.2020.101903 10.1016/j.media.2016.11.002 10.1007/s12021-014-9238-1 10.1186/s12859-017-1903-6 10.3389/fnagi.2015.00048 10.1109/JBHI.2015.2429556 10.1016/j.neuroimage.2011.09.069 10.1142/S012906572050032X 10.1038/srep26286 10.1006/nimg.2002.1132 10.1007/s00371-021-02328-7 10.1016/j.compmedimag.2019.101663 10.1109/JBHI.2020.2973324 10.1016/j.neucom.2019.04.066 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier Ltd |
| Copyright_xml | – notice: 2022 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.bspc.2022.103565 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1746-8108 |
| ExternalDocumentID | 10_1016_j_bspc_2022_103565 S1746809422000878 |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1~. 1~5 23N 4.4 457 4G. 5GY 5VS 6J9 7-5 71M 8P~ AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ABYKQ 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 AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG 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- AATTM AAXKI AAYWO AAYXX ABWVN ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AFXIZ AGCQF AGRNS AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP BNPGV CITATION SSH |
| ID | FETCH-LOGICAL-c230t-ddb08ef525fc4a9f363eab66cb2f5dfd1dd1cb61e27d1fce63e73ac365b56ff03 |
| IEDL.DBID | .~1 |
| ISSN | 1746-8094 |
| IngestDate | Tue Jul 01 01:34:13 EDT 2025 Thu Apr 24 23:13:24 EDT 2025 Fri Feb 23 02:39:54 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Deep learning Feature fusion MR images Convolutional neural networks Positron emission tomography Alzheimer’s disease detection |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c230t-ddb08ef525fc4a9f363eab66cb2f5dfd1dd1cb61e27d1fce63e73ac365b56ff03 |
| ParticipantIDs | crossref_primary_10_1016_j_bspc_2022_103565 crossref_citationtrail_10_1016_j_bspc_2022_103565 elsevier_sciencedirect_doi_10_1016_j_bspc_2022_103565 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | May 2022 2022-05-00 |
| PublicationDateYYYYMMDD | 2022-05-01 |
| PublicationDate_xml | – month: 05 year: 2022 text: May 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Biomedical signal processing and control |
| PublicationYear | 2022 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Shen (b0110) 2015 Silveira, Marques (b0085) 2010 Lu, Popuri, Ding, Balachandar, Beg, Weiner (b0165) 2018; 8 Zhu, Yuan, Huang, Hao, Zhang (b0170) 2019; 355 Li, Tran, Thung, Ji, Shen (b0160) 2015; 19 Gray (b0065) 2012; 60 Richhariya, Tanveer, Rashid (b0030) 2020; 59 Esther E Bron, Marion Smits, Wiesje M van der Flier, Hugo Vrenken, Frederik Barkhof, et al.. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage, Elsevier, 2015, 111, pp.562-79. Liu, Liu, Cai, Che, Pujol, Kikinis (b0150) 2015; 62 Zhu, Yin, Chai (b0080) 2018; 432 Milletari, Ahmadi, Kroll, Plate, Rozanski, Maiostre (b0140) 2017; 164 Gray (b0070) 2011; 2011 Korolev (b0055) 2017 Zhang M, Kong Z, Zhu W, et al. Pulmonary nodule detection based on 3D feature pyramid network with incorporated squeeze-and-excitation-attention mechanism. Concurrency and Computation: Practice and Experience, 2021: e6237. Gao, Pan, Li (b0025) 2017; 18 Abadi, Barham, Chen, Chen, Davis, Dean (b0145) 2016 Bi, Hu, Wu, Wang (b0180) 2020; 24 iek, zgun, et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Springer, Cham (2016). Liu M, Zhang J, Y ap PT, Shen D. View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data. Med Image Anal. (2017) 36:123-34. Zhan, Zhou, Wang (b0005) 2015; 7 Jenkinson, Bannister, Brady, Smith (b0135) 2002; 17 Feng, Halm-Lutterodt, Tang (b0050) 2020; 30 Christina (b0010) 2018 Sabuncu, Konukoglu (b0105) 2015; 13 Bartos, Gregus, Ibrahim (b0125) 2019; 287 Li Y, Wang Z, Yin L, et al. X-Net: a dual encoding-decoding method in medical image segmentation. 2021. doi: 10.1007/s00371-021-02328-7. Dolz, Desrosiers, Ayed (b0100) 2017 Zhang, Shen (b0045) 2012; 59 Tong, Gray, Gao, Chen, Rueckert (b0155) 2017; 63 Litjens G, Sanchez, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep;2016;6:26286. Shao, Peng, Zu, Wang, Zhang (b0175) 2020; 80 Jack, Bernstein, Fox, Thompson, Alexander, Harvey (b0115) 2008; 27 Fonov, Evans, Botteron, Almli, McKinstry, Collins (b0130) 2011; 54 Sanford (b0015) 2017; 33 Gao, Pan, Xie (b0040) 2016; 75 Hedayati, Khedmati, Taghipour-Gorjikolaie (b0035) 2021; 66 Liu, M., D. Cheng, and W. Yan. Classification of Alzheimer’s Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images. Front. Neuroinform. 12(2018):35. Zhu (10.1016/j.bspc.2022.103565_b0080) 2018; 432 Dolz (10.1016/j.bspc.2022.103565_b0100) 2017 Zhan (10.1016/j.bspc.2022.103565_b0005) 2015; 7 Lu (10.1016/j.bspc.2022.103565_b0165) 2018; 8 Gray (10.1016/j.bspc.2022.103565_b0070) 2011; 2011 Hedayati (10.1016/j.bspc.2022.103565_b0035) 2021; 66 Tong (10.1016/j.bspc.2022.103565_b0155) 2017; 63 10.1016/j.bspc.2022.103565_b0090 10.1016/j.bspc.2022.103565_b0075 10.1016/j.bspc.2022.103565_b0095 Fonov (10.1016/j.bspc.2022.103565_b0130) 2011; 54 Silveira (10.1016/j.bspc.2022.103565_b0085) 2010 Gray (10.1016/j.bspc.2022.103565_b0065) 2012; 60 Christina (10.1016/j.bspc.2022.103565_b0010) 2018 Li (10.1016/j.bspc.2022.103565_b0160) 2015; 19 Gao (10.1016/j.bspc.2022.103565_b0025) 2017; 18 Korolev (10.1016/j.bspc.2022.103565_b0055) 2017 Abadi (10.1016/j.bspc.2022.103565_b0145) 2016 10.1016/j.bspc.2022.103565_b0120 Liu (10.1016/j.bspc.2022.103565_b0150) 2015; 62 Sabuncu (10.1016/j.bspc.2022.103565_b0105) 2015; 13 Richhariya (10.1016/j.bspc.2022.103565_b0030) 2020; 59 Zhang (10.1016/j.bspc.2022.103565_b0045) 2012; 59 10.1016/j.bspc.2022.103565_b0060 10.1016/j.bspc.2022.103565_b0020 10.1016/j.bspc.2022.103565_b0185 Zhu (10.1016/j.bspc.2022.103565_b0170) 2019; 355 Sanford (10.1016/j.bspc.2022.103565_b0015) 2017; 33 Gao (10.1016/j.bspc.2022.103565_b0040) 2016; 75 Bi (10.1016/j.bspc.2022.103565_b0180) 2020; 24 Bartos (10.1016/j.bspc.2022.103565_b0125) 2019; 287 Milletari (10.1016/j.bspc.2022.103565_b0140) 2017; 164 Shao (10.1016/j.bspc.2022.103565_b0175) 2020; 80 Jack (10.1016/j.bspc.2022.103565_b0115) 2008; 27 Shen (10.1016/j.bspc.2022.103565_b0110) 2015 Jenkinson (10.1016/j.bspc.2022.103565_b0135) 2002; 17 Feng (10.1016/j.bspc.2022.103565_b0050) 2020; 30 |
| References_xml | – volume: 17 start-page: 825 year: 2002 end-page: 841 ident: b0135 article-title: Improved optimization for the robust and accurate linear registration and motion correction of brain images publication-title: Neuroimage – volume: 30 start-page: 2050032 year: 2020 ident: b0050 article-title: Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process publication-title: Int. J. Neural Syst. – year: 2018 ident: b0010 article-title: World Alzheimer report 2018 – reference: Li Y, Wang Z, Yin L, et al. X-Net: a dual encoding-decoding method in medical image segmentation. 2021. doi: 10.1007/s00371-021-02328-7. – volume: 19 start-page: 1610 year: 2015 end-page: 1616 ident: b0160 article-title: Li J.A robust deep model for improved classification of AD/MCI patients publication-title: IEEE J. Biomed. Health Inform. – volume: 75 start-page: 9333 year: 2016 end-page: 9369 ident: b0040 article-title: Graph modeling and mining methods for brain images publication-title: Multimed. Tools Appl. – volume: 24 start-page: 2973 year: 2020 end-page: 2983 ident: b0180 article-title: Multimodal data analysis of Alzheimer’s disease based on clustering evolutionary random forest publication-title: IEEE J. Biomed. Health Inform. – reference: iek, zgun, et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Springer, Cham (2016). – volume: 355 start-page: 143 year: 2019 end-page: 154 ident: b0170 article-title: Multi-modal AD classification via self-pacedlatent correlation analysis publication-title: Neurocomputing – volume: 2011 start-page: 1082 year: 2011 end-page: 1085 ident: b0070 article-title: Regional analysis of FDG-PET for use in the classification of Alzheimer’S Disease publication-title: IEEE International Symposium on Biomedical Imaging: From Nano to Macro – reference: Litjens G, Sanchez, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep;2016;6:26286. – reference: Liu M, Zhang J, Y ap PT, Shen D. View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data. Med Image Anal. (2017) 36:123-34. – volume: 164 start-page: 92 year: 2017 end-page: 102 ident: b0140 article-title: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound publication-title: Comput. Vis. Image Und. – volume: 59 start-page: 895 year: 2012 end-page: 907 ident: b0045 article-title: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease publication-title: Neuroimage – volume: 13 start-page: 31 year: 2015 end-page: 46 ident: b0105 article-title: Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study publication-title: Neuroinformatics – volume: 7 start-page: 48 year: 2015 ident: b0005 article-title: Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease publication-title: Front Aging Neurosci. – year: 2017 ident: b0100 article-title: 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study publication-title: Neuroimage – reference: Liu, M., D. Cheng, and W. Yan. Classification of Alzheimer’s Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images. Front. Neuroinform. 12(2018):35. – start-page: 265 year: 2016 end-page: 283 ident: b0145 article-title: TensorFlow: A system for large-scale machine learning publication-title: 12th USENIX Symposium on Operating Systems Design and Implementation – volume: 33 start-page: 325 year: 2017 end-page: 337 ident: b0015 article-title: Mild Cognitive Impairment publication-title: Clin. Geriatr. Med. – start-page: 2556 year: 2010 end-page: 2559 ident: b0085 article-title: Boosting Alzheimer Disease Diagnosis Using PET Images publication-title: 2010 20th International Conference on Pattern Recognition – volume: 8 start-page: 1 year: 2018 end-page: 13 ident: b0165 article-title: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images publication-title: Sci. Rep. – volume: 54 start-page: 313 year: 2011 end-page: 327 ident: b0130 article-title: Unbiased average age-appropriate atlases for pediatric studies publication-title: Neuroimage – volume: 62 start-page: 1132 year: 2015 end-page: 1140 ident: b0150 article-title: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease publication-title: IEEE Trans. Biomed. Eng. – volume: 59 year: 2020 ident: b0030 article-title: Diagnosis of Alzheimer’s disease using universum support vector machine based recursive feature elimination (USVM-RFE) publication-title: Biomed. Signal Process. Control – volume: 66 year: 2021 ident: b0035 article-title: Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer’s disease diagnosis publication-title: Biomed. Signal Process. Control – volume: 287 start-page: 70 year: 2019 end-page: 74 ident: b0125 article-title: Brain volumes and their ratios in Alzheimer’s disease on magnetic resonance imaging segmented using Freesurfer 6.0 publication-title: Psychiatry Res. Neuroimaging – start-page: 2251 year: 2015 end-page: 2254 ident: b0110 article-title: Semi-supervised manifold learning with affinity regularization for Alzheimer’s disease identification using positron emission tomography imaging publication-title: International Conference of the IEEE Engineering in Medicine & Biology Society IEEE – volume: 80 year: 2020 ident: b0175 article-title: Hypergraph based multi-task feature selection for multimodal classification of Alzheimer’s disease publication-title: Comput. Med. Imaging Graph. – volume: 27 start-page: 685 year: 2008 end-page: 691 ident: b0115 article-title: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods publication-title: J. Magn. Reson. Imaging – volume: 63 start-page: 171 year: 2017 end-page: 181 ident: b0155 article-title: Multi-modal classification of Alzheimer’s disease using nonlinear graph fusion publication-title: Pattern Recogn. – volume: 18 start-page: 505 year: 2017 ident: b0025 article-title: Brain medical image diagnosis based on corners with importance-values publication-title: BMC Bioinform. – reference: Esther E Bron, Marion Smits, Wiesje M van der Flier, Hugo Vrenken, Frederik Barkhof, et al.. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage, Elsevier, 2015, 111, pp.562-79. – volume: 60 start-page: 221 year: 2012 end-page: 229 ident: b0065 article-title: Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease publication-title: Neuroimage – volume: 432 start-page: 516 year: 2018 end-page: 529 ident: b0080 article-title: A novel multi-modality image fusion method based on image decomposition and sparse representation publication-title: Information ences – year: 2017 ident: b0055 article-title: Residual and plain convolutional neural networks for 3D brain MRI classification publication-title: IEEE International Symposium on Biomedical Imaging 2017 IEEE – reference: Zhang M, Kong Z, Zhu W, et al. Pulmonary nodule detection based on 3D feature pyramid network with incorporated squeeze-and-excitation-attention mechanism. Concurrency and Computation: Practice and Experience, 2021: e6237. – volume: 66 issue: 3 year: 2021 ident: 10.1016/j.bspc.2022.103565_b0035 article-title: Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer’s disease diagnosis publication-title: Biomed. Signal Process. Control – volume: 27 start-page: 685 year: 2008 ident: 10.1016/j.bspc.2022.103565_b0115 article-title: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.21049 – volume: 60 start-page: 221 issue: 1 year: 2012 ident: 10.1016/j.bspc.2022.103565_b0065 article-title: Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease publication-title: Neuroimage doi: 10.1016/j.neuroimage.2011.12.071 – volume: 287 start-page: 70 year: 2019 ident: 10.1016/j.bspc.2022.103565_b0125 article-title: Brain volumes and their ratios in Alzheimer’s disease on magnetic resonance imaging segmented using Freesurfer 6.0 publication-title: Psychiatry Res. Neuroimaging doi: 10.1016/j.pscychresns.2019.01.014 – volume: 54 start-page: 313 year: 2011 ident: 10.1016/j.bspc.2022.103565_b0130 article-title: Unbiased average age-appropriate atlases for pediatric studies publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.07.033 – volume: 62 start-page: 1132 year: 2015 ident: 10.1016/j.bspc.2022.103565_b0150 article-title: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2014.2372011 – ident: 10.1016/j.bspc.2022.103565_b0090 doi: 10.3389/fninf.2018.00035 – volume: 164 start-page: 92 year: 2017 ident: 10.1016/j.bspc.2022.103565_b0140 article-title: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound publication-title: Comput. Vis. Image Und. doi: 10.1016/j.cviu.2017.04.002 – volume: 75 start-page: 9333 year: 2016 ident: 10.1016/j.bspc.2022.103565_b0040 article-title: Graph modeling and mining methods for brain images publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-016-3482-3 – volume: 63 start-page: 171 year: 2017 ident: 10.1016/j.bspc.2022.103565_b0155 article-title: Multi-modal classification of Alzheimer’s disease using nonlinear graph fusion publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2016.10.009 – volume: 432 start-page: 516 year: 2018 ident: 10.1016/j.bspc.2022.103565_b0080 article-title: A novel multi-modality image fusion method based on image decomposition and sparse representation publication-title: Information ences – volume: 2011 start-page: 1082 year: 2011 ident: 10.1016/j.bspc.2022.103565_b0070 article-title: Regional analysis of FDG-PET for use in the classification of Alzheimer’S Disease publication-title: IEEE International Symposium on Biomedical Imaging: From Nano to Macro – ident: 10.1016/j.bspc.2022.103565_b0185 doi: 10.1002/cpe.6237 – volume: 33 start-page: 325 issue: 3 year: 2017 ident: 10.1016/j.bspc.2022.103565_b0015 article-title: Mild Cognitive Impairment publication-title: Clin. Geriatr. Med. doi: 10.1016/j.cger.2017.02.005 – ident: 10.1016/j.bspc.2022.103565_b0060 doi: 10.1016/j.neuroimage.2015.01.048 – volume: 59 year: 2020 ident: 10.1016/j.bspc.2022.103565_b0030 article-title: Diagnosis of Alzheimer’s disease using universum support vector machine based recursive feature elimination (USVM-RFE) publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2020.101903 – start-page: 2556 year: 2010 ident: 10.1016/j.bspc.2022.103565_b0085 article-title: Boosting Alzheimer Disease Diagnosis Using PET Images – ident: 10.1016/j.bspc.2022.103565_b0120 doi: 10.1016/j.media.2016.11.002 – volume: 13 start-page: 31 issue: 1 year: 2015 ident: 10.1016/j.bspc.2022.103565_b0105 article-title: Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study publication-title: Neuroinformatics doi: 10.1007/s12021-014-9238-1 – volume: 18 start-page: 505 issue: 1 year: 2017 ident: 10.1016/j.bspc.2022.103565_b0025 article-title: Brain medical image diagnosis based on corners with importance-values publication-title: BMC Bioinform. doi: 10.1186/s12859-017-1903-6 – year: 2018 ident: 10.1016/j.bspc.2022.103565_b0010 – year: 2017 ident: 10.1016/j.bspc.2022.103565_b0100 article-title: 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study publication-title: Neuroimage – volume: 7 start-page: 48 year: 2015 ident: 10.1016/j.bspc.2022.103565_b0005 article-title: Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease publication-title: Front Aging Neurosci. doi: 10.3389/fnagi.2015.00048 – year: 2017 ident: 10.1016/j.bspc.2022.103565_b0055 article-title: Residual and plain convolutional neural networks for 3D brain MRI classification – volume: 19 start-page: 1610 year: 2015 ident: 10.1016/j.bspc.2022.103565_b0160 article-title: Li J.A robust deep model for improved classification of AD/MCI patients publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2015.2429556 – volume: 59 start-page: 895 issue: 2 year: 2012 ident: 10.1016/j.bspc.2022.103565_b0045 article-title: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease publication-title: Neuroimage doi: 10.1016/j.neuroimage.2011.09.069 – start-page: 2251 year: 2015 ident: 10.1016/j.bspc.2022.103565_b0110 article-title: Semi-supervised manifold learning with affinity regularization for Alzheimer’s disease identification using positron emission tomography imaging publication-title: International Conference of the IEEE Engineering in Medicine & Biology Society IEEE – volume: 30 start-page: 2050032 issue: 6 year: 2020 ident: 10.1016/j.bspc.2022.103565_b0050 article-title: Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process publication-title: Int. J. Neural Syst. doi: 10.1142/S012906572050032X – ident: 10.1016/j.bspc.2022.103565_b0020 doi: 10.1038/srep26286 – volume: 17 start-page: 825 year: 2002 ident: 10.1016/j.bspc.2022.103565_b0135 article-title: Improved optimization for the robust and accurate linear registration and motion correction of brain images publication-title: Neuroimage doi: 10.1006/nimg.2002.1132 – ident: 10.1016/j.bspc.2022.103565_b0075 doi: 10.1007/s00371-021-02328-7 – ident: 10.1016/j.bspc.2022.103565_b0095 – volume: 80 year: 2020 ident: 10.1016/j.bspc.2022.103565_b0175 article-title: Hypergraph based multi-task feature selection for multimodal classification of Alzheimer’s disease publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2019.101663 – start-page: 265 year: 2016 ident: 10.1016/j.bspc.2022.103565_b0145 article-title: TensorFlow: A system for large-scale machine learning – volume: 24 start-page: 2973 year: 2020 ident: 10.1016/j.bspc.2022.103565_b0180 article-title: Multimodal data analysis of Alzheimer’s disease based on clustering evolutionary random forest publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2020.2973324 – volume: 355 start-page: 143 year: 2019 ident: 10.1016/j.bspc.2022.103565_b0170 article-title: Multi-modal AD classification via self-pacedlatent correlation analysis publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.04.066 – volume: 8 start-page: 1 year: 2018 ident: 10.1016/j.bspc.2022.103565_b0165 article-title: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images publication-title: Sci. Rep. |
| SSID | ssj0048714 |
| Score | 2.497319 |
| Snippet | Multi-modal medical imaging information has been widely used in computer-assisted investigations and diagnoses. A typical example is that the combination of... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 103565 |
| SubjectTerms | Alzheimer’s disease detection Convolutional neural networks Deep learning Feature fusion MR images Positron emission tomography |
| Title | Multi-modal data Alzheimer’s disease detection based on 3D convolution |
| URI | https://dx.doi.org/10.1016/j.bspc.2022.103565 |
| Volume | 75 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1NS8NAEF1KvehB_MT6UXLwJrFNNrtJjqVaomIvWugtZHdnsdKmpa0XD-Lf8O_5S9xJNqWC9OAt2cxCeBlmZsl7M4RcAg09oKBcGjEcYca0G2WAWpkwUBGXbVk0cX3s82QQ3A_ZsEa6lRYGaZU29pcxvYjWdqVl0WzNRqPWk6mleWROJz6qTaIQBb9BEKKXX3-saB6mHi_6e6Oxi9ZWOFNyvMRihm0MfR-15wwTzF_JaS3h9PbIrq0UnU75MvukBvkB2VnrH3hIkkI-606myhgi19PpjN9fYDSB-ffn18Kxf18cBcuCcZU7mLSUYy7ojYOEc-t4R2TQu33uJq4djeBKc2ZYukqJdgSa-UzLIIs15RQywbkUvmZKK08pTwrugR8qT0swj0OaScqZYFzrNj0m9XyawwlxzIIPoSdULP2AtoUIFDCIs5gDTrUKG8SrMEml7RuO4yvGaUUQe00RxxRxTEscG-RqtWdWds3YaM0qqNNf3z41YX3DvtN_7jsj23hX0hbPSX05f4MLU1osRbPwnSbZ6tw9JP0fZkfOsQ |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV09T8MwED1BGYAB8Sm-ycCGojZx7CRjBVQp0C60ElsU22dRVEoFZWHib_D3-CX4EgeBhDqwRfadFL1Yd2fl3TuAU2RxgAy1zxJOI8y48ZMCqVcmjnQiVEuVIq69vsiG0dUdv1uA87oXhmiVLvZXMb2M1m6l6dBsTkej5q2tpUVibychdZskcbIIS6ROxRuw1O5eZ_06INuSvJT4JnufHFzvTEXzki9TUjIMQ2o_55Rj_spPP3JOZx3WXLHotav32YAFnGzC6g8JwS3Iyg5a__FJW0Oie3rt8ds9jh7x-fP948VzP2A8jbOSdDXxKG9pzz6wC4845-7sbcOwczk4z3w3HcFX9tow87WWrQQND7lRUZEaJhgWUgglQ8O10YHWgZIiwDDWgVFot2NWKCa45MKYFtuBxuRpgrvg2YUQ40DqVIURa0kZaeSYFqlAGmwV70FQY5IrJx1OEyzGec0Re8gJx5xwzCsc9-Ds22daCWfMteY11Pmvz5_byD7Hb_-ffiewnA16N_lNt399ACu0U7EYD6Exe37FI1tpzOSxO0lfinzRYg |
| 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=Multi-modal+data+Alzheimer%E2%80%99s+disease+detection+based+on+3D+convolution&rft.jtitle=Biomedical+signal+processing+and+control&rft.au=Kong%2C+Zhaokai&rft.au=Zhang%2C+Mengyi&rft.au=Zhu%2C+Wenjun&rft.au=Yi%2C+Yang&rft.date=2022-05-01&rft.issn=1746-8094&rft.volume=75&rft.spage=103565&rft_id=info:doi/10.1016%2Fj.bspc.2022.103565&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_bspc_2022_103565 |
| 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 |