A simple self-supervised learning framework with patch-based data augmentation in diagnosis of Alzheimer’s disease
Alzheimer’s disease (AD) stands as a prominent age-related disorder with significant global impact. Utilizing computer-aided diagnosis aids in the timely identification of mild cognitive impairment, facilitating early intervention and management. Self-supervised learning models have attracted much a...
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| Vydané v: | Biomedical signal processing and control Ročník 96; s. 106572 |
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Elsevier Ltd
01.10.2024
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| ISSN: | 1746-8094 |
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| Abstract | Alzheimer’s disease (AD) stands as a prominent age-related disorder with significant global impact. Utilizing computer-aided diagnosis aids in the timely identification of mild cognitive impairment, facilitating early intervention and management. Self-supervised learning models have attracted much attention due to their advantages of no manual labeling, so they are very suitable for solving the problems of difficulty in data acquisition and high cost of manual labeling in medical image processing. However, the existing self-supervised algorithms applied to medical images often have poor diagnostic effects and consume large resources, as a consequence, the model’s ability to learn meaningful representations from medical images is hampered, leading to suboptimal performance. Subsequently, diverse patches extracted from the same brain are amalgamated to create contrast views, and attention weights are then employed to enhance the fitting and generalization capacity of the model. Experimental results on the ADNI dataset with 1365 subjects show that PD-SIM has been improved in the diagnosis of different diseases(such as the classification ACC of AD and CN reached 0.797, and the classification ACC of early cognitive impairment reached 0.7036), and also alleviates the problem of large consumption of computer resources, downstream tasks require only 52ms per image. The proposed method performs well in atrophic structure identification and AD diagnosis. Therefore, PD-SIM has a wide range of application prospects and is of great practical significance within the realm of medical image analysis. The data and code can be found at https://github.com/Z1Ting/PD-SIM.
•A simple contrastive learning framework is proposed to recombine local microstructures into global features.•A patch-based strategy is introduced to filter the normal regions and generate multiple key local regions.•The spatial attention mechanism is integrated to make the model pay better attention to the lesion area. |
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| AbstractList | Alzheimer’s disease (AD) stands as a prominent age-related disorder with significant global impact. Utilizing computer-aided diagnosis aids in the timely identification of mild cognitive impairment, facilitating early intervention and management. Self-supervised learning models have attracted much attention due to their advantages of no manual labeling, so they are very suitable for solving the problems of difficulty in data acquisition and high cost of manual labeling in medical image processing. However, the existing self-supervised algorithms applied to medical images often have poor diagnostic effects and consume large resources, as a consequence, the model’s ability to learn meaningful representations from medical images is hampered, leading to suboptimal performance. Subsequently, diverse patches extracted from the same brain are amalgamated to create contrast views, and attention weights are then employed to enhance the fitting and generalization capacity of the model. Experimental results on the ADNI dataset with 1365 subjects show that PD-SIM has been improved in the diagnosis of different diseases(such as the classification ACC of AD and CN reached 0.797, and the classification ACC of early cognitive impairment reached 0.7036), and also alleviates the problem of large consumption of computer resources, downstream tasks require only 52ms per image. The proposed method performs well in atrophic structure identification and AD diagnosis. Therefore, PD-SIM has a wide range of application prospects and is of great practical significance within the realm of medical image analysis. The data and code can be found at https://github.com/Z1Ting/PD-SIM.
•A simple contrastive learning framework is proposed to recombine local microstructures into global features.•A patch-based strategy is introduced to filter the normal regions and generate multiple key local regions.•The spatial attention mechanism is integrated to make the model pay better attention to the lesion area. |
| ArticleNumber | 106572 |
| Author | Wang, Jinfeng Huang, Shuaihui Wang, Zhiwen Gong, Haoqiang |
| Author_xml | – sequence: 1 givenname: Haoqiang surname: Gong fullname: Gong, Haoqiang – sequence: 2 givenname: Zhiwen surname: Wang fullname: Wang, Zhiwen – sequence: 3 givenname: Shuaihui surname: Huang fullname: Huang, Shuaihui – sequence: 4 givenname: Jinfeng orcidid: 0000-0002-1246-4617 surname: Wang fullname: Wang, Jinfeng email: wangjinfeng@scau.edu.cn |
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| Cites_doi | 10.1016/j.neuroimage.2010.06.013 10.1016/j.neuroimage.2019.116459 10.1126/science.1127647 10.1016/j.media.2020.101694 10.1109/TPAMI.2012.142 10.3389/fnins.2019.00509 10.1109/MSP.2017.2765202 10.1016/j.neuroimage.2023.120485 10.1177/1533317508328602 10.1016/j.neucom.2020.01.053 10.1007/BF00308809 10.1016/j.neuroimage.2005.09.046 10.1109/CVPR.2018.00745 10.1016/j.compmedimag.2018.09.009 10.1016/j.compbiomed.2017.10.002 10.1109/CVPR46437.2021.01549 10.3390/fractalfract7080598 10.1006/nimg.2001.0848 10.1001/jama.2009.1064 10.1016/0730-725X(88)90401-8 10.1109/CVPR.2016.90 10.1016/j.neuroimage.2011.12.029 10.1109/CVPR.2019.00198 10.1016/j.jalz.2011.03.003 10.1016/j.neuroimage.2014.06.077 10.1093/brain/awaa137 10.1109/CVPR42600.2020.00975 10.1145/3422622 10.1097/00004728-199803000-00032 10.1109/TMI.2021.3077079 10.1007/978-3-030-01234-2_1 10.1016/S1474-4422(12)70291-0 10.1016/j.media.2018.10.012 10.1109/TMI.2021.3079709 10.1016/j.media.2021.102051 10.1038/nrneurol.2009.215 10.1016/S0197-4580(01)00230-5 10.1016/j.neuroimage.2010.04.241 10.1016/j.media.2022.102571 10.1016/j.neucom.2018.12.018 10.1109/TMI.2016.2582386 10.1109/TPAMI.2020.2992393 10.3390/technologies9010002 |
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| Keywords | Attention mechanism Data augmentation Alzheimer’s disease Self-supervised learning MRI brain patch |
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| References | 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. Gidaris, Singh, Komodakis (b44) 2018 Creswell, White, Dumoulin, Arulkumaran, Sengupta, Bharath (b38) 2018; 35 Özçelik, Altan (b10) 2023; 7 Devlin, Chang, Lee, Toutanova (b41) 2018 S. Woo, J. Park, J. Lee, I.S. Kweon, Cbam: Convolutional block attention module, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 3–19. Liu, Li, Yan, Wang, Ma, Shen, Xu (b5) 2020; 208 Wang, Fan, Wang, Jiao, Schiele (b47) 2018 Cuingnet, Glaunès, Chupin, Benali, Colliot (b22) 2012; 35 Deng, Dong, Socher, Li, Li, Li (b20) 2009 De Santi, de Leon, Rusinek, Convit, Tarshish, Roche, Tsui, Kandil, Boppana, Daisley (b25) 2001; 22 Narayana, Brey, Kulkarni, Sievenpiper (b51) 1988; 6 Ouyang, Zhao, Adeli, Zaharchuk, Pohl (b56) 2022; 82 Masci, Meier, Ciresan, Schmidhuber (b13) 2011 Li, Lin, Chen (b24) 2020; 388 Baron, Chételat, Desgranges, Perchey, Landeau, de La Sayette, Eustache (b31) 2001; 14 Chen, Kornblith, Norouzi, Hinton (b17) 2020 Xu, Liu, Luo, Hu, Shen, Du, Kuang, Yang (b49) 2023 Holmes, Hoge, Collins, Woods, Toga, Evans (b52) 1998; 22 Huang, Xu, Zhou, Tong, Zhuang (b7) 2019; 13 Mattsson, Zetterberg, Hansson, Andreasen, Parnetti, Jonsson, Herukka, van der Flier, Blankenstein, Ewers (b26) 2009; 302 Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (b14) 2020; 63 Braak, Braak (b54) 1991; 82 Özçelik, Altan (b11) 2023 Sperling, Aisen, Beckett, Bennett, Craft, Fagan, Iwatsubo, Jack, Kaye, Montine (b27) 2011; 7 Ouyang, Biffi, Chen, Kart, Qiu, Rueckert (b19) 2020 Dhinagar, Thomopoulos, Rajagopalan, Stripelis, Ambite, Ver Steeg, Thompson (b58) 2023; Vol. 12567 Fang, Zhang, Nie, Cao, Rekik, Lee, He, Shen (b6) 2019; 51 Wang, Shen, Wang, Xiao, Deng, Wang, Zhao (b8) 2019; 333 Jiang, Miao (b18) 2022 Zhu, Sun, Huang, Han, Zhang (b15) 2021; 40 Y. Özçelik, A. Altan, Classification of diabetic retinopathy by machine learning algorithm using entorpy-based features. Jaiswal, Babu, Zadeh, Banerjee, Makedon (b39) 2020; 9 Xu, Ba, Kiros, Cho, Courville, Salakhudinov, Zemel, Bengio (b45) 2015 Matsuda (b32) 2013; 4 X. Zhan, X. Pan, Z. Liu, D. Lin, C.C. Loy, Self-supervised learning via conditional motion propagation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 1881–1889. X. Chen, K. He, Exploring simple siamese representation learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 15750–15758. Wang, Jiang, Li, Xu, Deng, Dai, Xu, Li, Guo, Wang (b16) 2021; 40 J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132–7141. Aderghal, Khvostikov, Krylov, Benois-Pineau, Afdel, Catheline (b3) 2018 Vounou, Janousova, Wolz, Stein, Thompson, Rueckert, Montana (b29) 2012; 60 Jovicich, Czanner, Greve, Haley, van Der Kouwe, Gollub, Kennedy, Schmitt, Brown, MacFall (b50) 2006; 30 Polzehl, Voss, Tabelow (b53) 2010; 52 K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. Ludovico, Trudi, MariaGrazia, Giorgio (b1) 2009; 24 Zhang, Gao, Gao, Munsell, Shen (b34) 2016; 35 Shangran, Joshi, Miller, Chonghua, Xiao, Cody, H, Joshi, Brigid, Shuhan (b35) 2020; 143 Cao, Liu, Yang, Zhao, Huang, Zhang, Zaiane (b33) 2017; 91 Wen, Elina, Mauricio, SamperGonzález, Colliot (b2) 2020; 63 Zhao, Liu, Adeli, Pohl (b57) 2021; 71 Cuingnet, Gerardin, Tessieras, Auzias, Lehéricy, Habert, Chupin, Benali, Colliot (b30) 2011; 56 Jing, Tian (b36) 2020; 43 ZhiPei, Lauterbur (b28) 2000 Suk, Lee, Shen (b9) 2014; 101 Frisoni, Fox, Jack, Scheltens, Thompson (b23) 2010; 6 Hinton, Salakhutdinov (b37) 2006; 313 Fedorov, Geenjaar, Wu, Sylvain, DeRamus, Luck, Misiura, Mittapalle, Hjelm, Plis (b59) 2024; 285 Li, Liu (b4) 2018; 70 Jack, Knopman, Jagust, Petersen, Weiner, Aisen, Shaw, Vemuri, Wiste, Weigand (b55) 2013; 12 Özçelik (10.1016/j.bspc.2024.106572_b10) 2023; 7 Wang (10.1016/j.bspc.2024.106572_b8) 2019; 333 10.1016/j.bspc.2024.106572_b12 Wang (10.1016/j.bspc.2024.106572_b47) 2018 Dhinagar (10.1016/j.bspc.2024.106572_b58) 2023; Vol. 12567 Zhang (10.1016/j.bspc.2024.106572_b34) 2016; 35 Xu (10.1016/j.bspc.2024.106572_b49) 2023 10.1016/j.bspc.2024.106572_b21 Jing (10.1016/j.bspc.2024.106572_b36) 2020; 43 Holmes (10.1016/j.bspc.2024.106572_b52) 1998; 22 Gidaris (10.1016/j.bspc.2024.106572_b44) 2018 Huang (10.1016/j.bspc.2024.106572_b7) 2019; 13 Cuingnet (10.1016/j.bspc.2024.106572_b30) 2011; 56 Xu (10.1016/j.bspc.2024.106572_b45) 2015 Zhao (10.1016/j.bspc.2024.106572_b57) 2021; 71 10.1016/j.bspc.2024.106572_b48 Braak (10.1016/j.bspc.2024.106572_b54) 1991; 82 10.1016/j.bspc.2024.106572_b46 Wen (10.1016/j.bspc.2024.106572_b2) 2020; 63 Deng (10.1016/j.bspc.2024.106572_b20) 2009 Suk (10.1016/j.bspc.2024.106572_b9) 2014; 101 Hinton (10.1016/j.bspc.2024.106572_b37) 2006; 313 Liu (10.1016/j.bspc.2024.106572_b5) 2020; 208 Jiang (10.1016/j.bspc.2024.106572_b18) 2022 Baron (10.1016/j.bspc.2024.106572_b31) 2001; 14 Fedorov (10.1016/j.bspc.2024.106572_b59) 2024; 285 Aderghal (10.1016/j.bspc.2024.106572_b3) 2018 Sperling (10.1016/j.bspc.2024.106572_b27) 2011; 7 Zhu (10.1016/j.bspc.2024.106572_b15) 2021; 40 Goodfellow (10.1016/j.bspc.2024.106572_b14) 2020; 63 Li (10.1016/j.bspc.2024.106572_b24) 2020; 388 Shangran (10.1016/j.bspc.2024.106572_b35) 2020; 143 Masci (10.1016/j.bspc.2024.106572_b13) 2011 Özçelik (10.1016/j.bspc.2024.106572_b11) 2023 Narayana (10.1016/j.bspc.2024.106572_b51) 1988; 6 Fang (10.1016/j.bspc.2024.106572_b6) 2019; 51 Cuingnet (10.1016/j.bspc.2024.106572_b22) 2012; 35 Devlin (10.1016/j.bspc.2024.106572_b41) 2018 Ouyang (10.1016/j.bspc.2024.106572_b56) 2022; 82 Jack (10.1016/j.bspc.2024.106572_b55) 2013; 12 Polzehl (10.1016/j.bspc.2024.106572_b53) 2010; 52 Wang (10.1016/j.bspc.2024.106572_b16) 2021; 40 Ouyang (10.1016/j.bspc.2024.106572_b19) 2020 10.1016/j.bspc.2024.106572_b42 10.1016/j.bspc.2024.106572_b43 Chen (10.1016/j.bspc.2024.106572_b17) 2020 De Santi (10.1016/j.bspc.2024.106572_b25) 2001; 22 10.1016/j.bspc.2024.106572_b40 Frisoni (10.1016/j.bspc.2024.106572_b23) 2010; 6 Li (10.1016/j.bspc.2024.106572_b4) 2018; 70 Creswell (10.1016/j.bspc.2024.106572_b38) 2018; 35 Mattsson (10.1016/j.bspc.2024.106572_b26) 2009; 302 Jaiswal (10.1016/j.bspc.2024.106572_b39) 2020; 9 Cao (10.1016/j.bspc.2024.106572_b33) 2017; 91 Jovicich (10.1016/j.bspc.2024.106572_b50) 2006; 30 Matsuda (10.1016/j.bspc.2024.106572_b32) 2013; 4 Ludovico (10.1016/j.bspc.2024.106572_b1) 2009; 24 ZhiPei (10.1016/j.bspc.2024.106572_b28) 2000 Vounou (10.1016/j.bspc.2024.106572_b29) 2012; 60 |
| References_xml | – start-page: 52 year: 2011 end-page: 59 ident: b13 article-title: Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction – volume: 82 start-page: 239 year: 1991 end-page: 259 ident: b54 article-title: Neuropathological stageing of Alzheimer-related changes publication-title: Acta Neuropathol. – volume: 82 year: 2022 ident: b56 article-title: Self-supervised learning of neighborhood embedding for longitudinal MRI publication-title: Med. Image Anal. – volume: 35 start-page: 53 year: 2018 end-page: 65 ident: b38 article-title: Generative adversarial networks: An overview publication-title: IEEE Signal Process. Mag. – volume: 12 start-page: 207 year: 2013 end-page: 216 ident: b55 article-title: Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers publication-title: Lancet Neurol. – volume: 63 start-page: 139 year: 2020 end-page: 144 ident: b14 article-title: Generative adversarial networks publication-title: Commun. ACM – volume: 40 start-page: 2354 year: 2021 end-page: 2366 ident: b15 article-title: Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI publication-title: IEEE Trans. Med. Imaging – start-page: 2048 year: 2015 end-page: 2057 ident: b45 article-title: Show, attend and tell: Neural image caption generation with visual attention publication-title: International Conference on Machine Learning – volume: 40 start-page: 2463 year: 2021 end-page: 2476 ident: b16 article-title: Joint learning of 3D lesion segmentation and classification for explainable COVID-19 diagnosis publication-title: IEEE Trans. Med. Imaging – volume: 388 start-page: 280 year: 2020 end-page: 287 ident: b24 article-title: Detecting Alzheimer’s disease based on 4D fMRI: An exploration under deep learning framework publication-title: Neurocomputing – year: 2018 ident: b41 article-title: Bert: Pre-training of deep bidirectional transformers for language understanding – volume: 22 start-page: 529 year: 2001 end-page: 539 ident: b25 article-title: Hippocampal formation glucose metabolism and volume losses in MCI and AD publication-title: Neurobiol. Aging – start-page: 1 year: 2000 end-page: 416 ident: b28 article-title: Principles of magnetic resonance imaging: A signal processing approach – volume: 285 year: 2024 ident: b59 article-title: Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links publication-title: NeuroImage – volume: 7 start-page: 598 year: 2023 ident: b10 article-title: Overcoming nonlinear dynamics in diabetic retinopathy classification: a robust AI-based model with chaotic swarm intelligence optimization and recurrent long short-term memory publication-title: Fractal Fract. – volume: 56 start-page: 766 year: 2011 end-page: 781 ident: b30 article-title: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database publication-title: Neuroimage – volume: 6 start-page: 271 year: 1988 end-page: 274 ident: b51 article-title: Compensation for surface coil sensitivity variation in magnetic resonance imaging publication-title: Magn. Reson. Imaging – volume: 51 start-page: 157 year: 2019 end-page: 168 ident: b6 article-title: Automatic brain labeling via multi-atlas guided fully convolutional networks publication-title: Med. Image Anal. – reference: X. Zhan, X. Pan, Z. Liu, D. Lin, C.C. Loy, Self-supervised learning via conditional motion propagation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 1881–1889. – start-page: 345 year: 2018 end-page: 350 ident: b3 article-title: Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning – volume: 14 start-page: 298 year: 2001 end-page: 309 ident: b31 article-title: In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer’s disease publication-title: Neuroimage – volume: Vol. 12567 start-page: 504 year: 2023 end-page: 513 ident: b58 article-title: Evaluation of transfer learning methods for detecting alzheimer’s disease with brain MRI publication-title: 18th International Symposium on Medical Information Processing and Analysis – volume: 6 start-page: 67 year: 2010 end-page: 77 ident: b23 article-title: The clinical use of structural MRI in Alzheimer disease publication-title: Nat. Rev. Neurol. – volume: 35 start-page: 682 year: 2012 end-page: 696 ident: b22 article-title: Spatial and anatomical regularization of SVM: a general framework for neuroimaging data publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 60 start-page: 700 year: 2012 end-page: 716 ident: b29 article-title: Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease publication-title: Neuroimage – reference: K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. – year: 2018 ident: b47 article-title: Parameter-free spatial attention network for person re-identification – volume: 43 start-page: 4037 year: 2020 end-page: 4058 ident: b36 article-title: Self-supervised visual feature learning with deep neural networks: A survey publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 13 start-page: 509 year: 2019 ident: b7 article-title: Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network publication-title: Front. Neurosci. – volume: 30 start-page: 436 year: 2006 end-page: 443 ident: b50 article-title: Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data publication-title: Neuroimage – 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: 71 year: 2021 ident: b57 article-title: LSSL: Longitudinal self-supervised learning publication-title: Med. Image Anal. – volume: 24 start-page: 95 year: 2009 end-page: 121 ident: b1 article-title: Reviews: Current concepts in Alzheimer’s disease: A multidisciplinary review publication-title: Am. J. Alzheimer’s Dis. Other Dementias® – volume: 35 start-page: 2524 year: 2016 end-page: 2533 ident: b34 article-title: Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis publication-title: IEEE Trans. Med. Imaging – volume: 9 start-page: 2 year: 2020 ident: b39 article-title: A survey on contrastive self-supervised learning publication-title: Technologies – start-page: 1 year: 2022 end-page: 8 ident: b18 article-title: Pre-training 3D convolutional neural networks for prodromal Alzheimer’s disease classification publication-title: 2022 International Joint Conference on Neural Networks – volume: 52 start-page: 515 year: 2010 end-page: 523 ident: b53 article-title: Structural adaptive segmentation for statistical parametric mapping publication-title: NeuroImage – volume: 91 start-page: 21 year: 2017 end-page: 37 ident: b33 article-title: Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures publication-title: Comput. Biol. Med. – reference: J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132–7141. – start-page: 762 year: 2020 end-page: 780 ident: b19 article-title: Self-supervision with superpixels: Training few-shot medical image segmentation without annotation publication-title: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16 – volume: 70 start-page: 101 year: 2018 end-page: 110 ident: b4 article-title: Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks publication-title: Comput. Med. Imaging Graph. – volume: 7 start-page: 280 year: 2011 end-page: 292 ident: b27 article-title: Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease publication-title: Alzheimer’s Dementia – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: b37 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – start-page: 1597 year: 2020 end-page: 1607 ident: b17 article-title: A simple framework for contrastive learning of visual representations publication-title: International Conference on Machine Learning – year: 2018 ident: b44 article-title: Unsupervised representation learning by predicting image rotations – volume: 208 year: 2020 ident: b5 article-title: A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease publication-title: Neuroimage – volume: 22 start-page: 324 year: 1998 end-page: 333 ident: b52 article-title: Enhancement of MR images using registration for signal averaging publication-title: J. Comput. Assist. Tomogr. – volume: 4 start-page: 29 year: 2013 ident: b32 article-title: Voxel-based morphometry of brain MRI in normal aging and Alzheimer’s disease publication-title: Aging Dis. – volume: 101 start-page: 569 year: 2014 end-page: 582 ident: b9 article-title: Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis publication-title: NeuroImage – reference: S. Woo, J. Park, J. Lee, I.S. Kweon, Cbam: Convolutional block attention module, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 3–19. – volume: 63 year: 2020 ident: b2 article-title: Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation publication-title: Med. Image Anal. – year: 2023 ident: b49 article-title: SGDA: Towards 3D universal pulmonary nodule detection via slice grouped domain attention publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. – volume: 143 start-page: 1920 year: 2020 end-page: 1933 ident: b35 article-title: Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification publication-title: Brain – start-page: 1 year: 2023 end-page: 5 ident: b11 article-title: A comparative analysis of artificial intelligence optimization algorithms for the selection of entropy-based features in the early detection of epileptic seizures publication-title: 2023 14th International Conference on Electrical and Electronics Engineering – reference: X. Chen, K. He, Exploring simple siamese representation learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 15750–15758. – volume: 302 start-page: 385 year: 2009 end-page: 393 ident: b26 article-title: CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment publication-title: Jama – volume: 333 start-page: 145 year: 2019 end-page: 156 ident: b8 article-title: Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease publication-title: Neurocomputing – start-page: 248 year: 2009 end-page: 255 ident: b20 article-title: Imagenet: A Large-Scale Hierarchical Image Database – reference: Y. Özçelik, A. Altan, Classification of diabetic retinopathy by machine learning algorithm using entorpy-based features. – volume: 56 start-page: 766 issue: 2 year: 2011 ident: 10.1016/j.bspc.2024.106572_b30 article-title: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.06.013 – volume: 208 year: 2020 ident: 10.1016/j.bspc.2024.106572_b5 article-title: A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.116459 – start-page: 1 year: 2023 ident: 10.1016/j.bspc.2024.106572_b11 article-title: A comparative analysis of artificial intelligence optimization algorithms for the selection of entropy-based features in the early detection of epileptic seizures – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 10.1016/j.bspc.2024.106572_b37 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 63 year: 2020 ident: 10.1016/j.bspc.2024.106572_b2 article-title: Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation publication-title: Med. Image Anal. doi: 10.1016/j.media.2020.101694 – volume: 35 start-page: 682 issue: 3 year: 2012 ident: 10.1016/j.bspc.2024.106572_b22 article-title: Spatial and anatomical regularization of SVM: a general framework for neuroimaging data publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.142 – volume: 13 start-page: 509 year: 2019 ident: 10.1016/j.bspc.2024.106572_b7 article-title: Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network publication-title: Front. Neurosci. doi: 10.3389/fnins.2019.00509 – start-page: 1597 year: 2020 ident: 10.1016/j.bspc.2024.106572_b17 article-title: A simple framework for contrastive learning of visual representations – volume: 35 start-page: 53 issue: 1 year: 2018 ident: 10.1016/j.bspc.2024.106572_b38 article-title: Generative adversarial networks: An overview publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2017.2765202 – volume: 285 year: 2024 ident: 10.1016/j.bspc.2024.106572_b59 article-title: Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links publication-title: NeuroImage doi: 10.1016/j.neuroimage.2023.120485 – volume: 24 start-page: 95 issue: 2 year: 2009 ident: 10.1016/j.bspc.2024.106572_b1 article-title: Reviews: Current concepts in Alzheimer’s disease: A multidisciplinary review publication-title: Am. J. Alzheimer’s Dis. Other Dementias® doi: 10.1177/1533317508328602 – volume: 388 start-page: 280 year: 2020 ident: 10.1016/j.bspc.2024.106572_b24 article-title: Detecting Alzheimer’s disease based on 4D fMRI: An exploration under deep learning framework publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.01.053 – volume: 82 start-page: 239 issue: 4 year: 1991 ident: 10.1016/j.bspc.2024.106572_b54 article-title: Neuropathological stageing of Alzheimer-related changes publication-title: Acta Neuropathol. doi: 10.1007/BF00308809 – volume: 30 start-page: 436 issue: 2 year: 2006 ident: 10.1016/j.bspc.2024.106572_b50 article-title: Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data publication-title: Neuroimage doi: 10.1016/j.neuroimage.2005.09.046 – ident: 10.1016/j.bspc.2024.106572_b48 doi: 10.1109/CVPR.2018.00745 – volume: 70 start-page: 101 year: 2018 ident: 10.1016/j.bspc.2024.106572_b4 article-title: Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2018.09.009 – volume: 91 start-page: 21 year: 2017 ident: 10.1016/j.bspc.2024.106572_b33 article-title: Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.10.002 – ident: 10.1016/j.bspc.2024.106572_b43 doi: 10.1109/CVPR46437.2021.01549 – volume: 7 start-page: 598 issue: 8 year: 2023 ident: 10.1016/j.bspc.2024.106572_b10 article-title: Overcoming nonlinear dynamics in diabetic retinopathy classification: a robust AI-based model with chaotic swarm intelligence optimization and recurrent long short-term memory publication-title: Fractal Fract. doi: 10.3390/fractalfract7080598 – volume: 14 start-page: 298 issue: 2 year: 2001 ident: 10.1016/j.bspc.2024.106572_b31 article-title: In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer’s disease publication-title: Neuroimage doi: 10.1006/nimg.2001.0848 – volume: Vol. 12567 start-page: 504 year: 2023 ident: 10.1016/j.bspc.2024.106572_b58 article-title: Evaluation of transfer learning methods for detecting alzheimer’s disease with brain MRI – volume: 302 start-page: 385 issue: 4 year: 2009 ident: 10.1016/j.bspc.2024.106572_b26 article-title: CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment publication-title: Jama doi: 10.1001/jama.2009.1064 – year: 2018 ident: 10.1016/j.bspc.2024.106572_b41 – volume: 6 start-page: 271 issue: 3 year: 1988 ident: 10.1016/j.bspc.2024.106572_b51 article-title: Compensation for surface coil sensitivity variation in magnetic resonance imaging publication-title: Magn. Reson. Imaging doi: 10.1016/0730-725X(88)90401-8 – start-page: 1 year: 2022 ident: 10.1016/j.bspc.2024.106572_b18 article-title: Pre-training 3D convolutional neural networks for prodromal Alzheimer’s disease classification – ident: 10.1016/j.bspc.2024.106572_b21 doi: 10.1109/CVPR.2016.90 – volume: 60 start-page: 700 issue: 1 year: 2012 ident: 10.1016/j.bspc.2024.106572_b29 article-title: Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease publication-title: Neuroimage doi: 10.1016/j.neuroimage.2011.12.029 – ident: 10.1016/j.bspc.2024.106572_b40 doi: 10.1109/CVPR.2019.00198 – volume: 7 start-page: 280 issue: 3 year: 2011 ident: 10.1016/j.bspc.2024.106572_b27 article-title: Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease publication-title: Alzheimer’s Dementia doi: 10.1016/j.jalz.2011.03.003 – start-page: 52 year: 2011 ident: 10.1016/j.bspc.2024.106572_b13 – volume: 101 start-page: 569 year: 2014 ident: 10.1016/j.bspc.2024.106572_b9 article-title: Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis publication-title: NeuroImage doi: 10.1016/j.neuroimage.2014.06.077 – volume: 143 start-page: 1920 issue: 6 year: 2020 ident: 10.1016/j.bspc.2024.106572_b35 article-title: Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification publication-title: Brain doi: 10.1093/brain/awaa137 – start-page: 762 year: 2020 ident: 10.1016/j.bspc.2024.106572_b19 article-title: Self-supervision with superpixels: Training few-shot medical image segmentation without annotation – ident: 10.1016/j.bspc.2024.106572_b42 doi: 10.1109/CVPR42600.2020.00975 – ident: 10.1016/j.bspc.2024.106572_b12 – volume: 63 start-page: 139 issue: 11 year: 2020 ident: 10.1016/j.bspc.2024.106572_b14 article-title: Generative adversarial networks publication-title: Commun. ACM doi: 10.1145/3422622 – volume: 22 start-page: 324 issue: 2 year: 1998 ident: 10.1016/j.bspc.2024.106572_b52 article-title: Enhancement of MR images using registration for signal averaging publication-title: J. Comput. Assist. Tomogr. doi: 10.1097/00004728-199803000-00032 – year: 2018 ident: 10.1016/j.bspc.2024.106572_b47 – year: 2023 ident: 10.1016/j.bspc.2024.106572_b49 article-title: SGDA: Towards 3D universal pulmonary nodule detection via slice grouped domain attention publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. – volume: 40 start-page: 2354 issue: 9 year: 2021 ident: 10.1016/j.bspc.2024.106572_b15 article-title: Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2021.3077079 – ident: 10.1016/j.bspc.2024.106572_b46 doi: 10.1007/978-3-030-01234-2_1 – volume: 12 start-page: 207 issue: 2 year: 2013 ident: 10.1016/j.bspc.2024.106572_b55 article-title: Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers publication-title: Lancet Neurol. doi: 10.1016/S1474-4422(12)70291-0 – volume: 4 start-page: 29 issue: 1 year: 2013 ident: 10.1016/j.bspc.2024.106572_b32 article-title: Voxel-based morphometry of brain MRI in normal aging and Alzheimer’s disease publication-title: Aging Dis. – volume: 51 start-page: 157 year: 2019 ident: 10.1016/j.bspc.2024.106572_b6 article-title: Automatic brain labeling via multi-atlas guided fully convolutional networks publication-title: Med. Image Anal. doi: 10.1016/j.media.2018.10.012 – volume: 40 start-page: 2463 issue: 9 year: 2021 ident: 10.1016/j.bspc.2024.106572_b16 article-title: Joint learning of 3D lesion segmentation and classification for explainable COVID-19 diagnosis publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2021.3079709 – volume: 71 year: 2021 ident: 10.1016/j.bspc.2024.106572_b57 article-title: LSSL: Longitudinal self-supervised learning publication-title: Med. Image Anal. doi: 10.1016/j.media.2021.102051 – volume: 6 start-page: 67 issue: 2 year: 2010 ident: 10.1016/j.bspc.2024.106572_b23 article-title: The clinical use of structural MRI in Alzheimer disease publication-title: Nat. Rev. Neurol. doi: 10.1038/nrneurol.2009.215 – volume: 22 start-page: 529 issue: 4 year: 2001 ident: 10.1016/j.bspc.2024.106572_b25 article-title: Hippocampal formation glucose metabolism and volume losses in MCI and AD publication-title: Neurobiol. Aging doi: 10.1016/S0197-4580(01)00230-5 – volume: 52 start-page: 515 issue: 2 year: 2010 ident: 10.1016/j.bspc.2024.106572_b53 article-title: Structural adaptive segmentation for statistical parametric mapping publication-title: NeuroImage doi: 10.1016/j.neuroimage.2010.04.241 – volume: 82 year: 2022 ident: 10.1016/j.bspc.2024.106572_b56 article-title: Self-supervised learning of neighborhood embedding for longitudinal MRI publication-title: Med. Image Anal. doi: 10.1016/j.media.2022.102571 – start-page: 2048 year: 2015 ident: 10.1016/j.bspc.2024.106572_b45 article-title: Show, attend and tell: Neural image caption generation with visual attention – volume: 333 start-page: 145 year: 2019 ident: 10.1016/j.bspc.2024.106572_b8 article-title: Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.12.018 – year: 2018 ident: 10.1016/j.bspc.2024.106572_b44 – start-page: 248 year: 2009 ident: 10.1016/j.bspc.2024.106572_b20 – volume: 35 start-page: 2524 issue: 12 year: 2016 ident: 10.1016/j.bspc.2024.106572_b34 article-title: Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2582386 – volume: 43 start-page: 4037 issue: 11 year: 2020 ident: 10.1016/j.bspc.2024.106572_b36 article-title: Self-supervised visual feature learning with deep neural networks: A survey publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2020.2992393 – start-page: 345 year: 2018 ident: 10.1016/j.bspc.2024.106572_b3 – start-page: 1 year: 2000 ident: 10.1016/j.bspc.2024.106572_b28 – volume: 9 start-page: 2 issue: 1 year: 2020 ident: 10.1016/j.bspc.2024.106572_b39 article-title: A survey on contrastive self-supervised learning publication-title: Technologies doi: 10.3390/technologies9010002 |
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