Classification of Alzheimer’s disease based on multi-example learning and multi-scale feature fusion_Journal of Biomedical Engineering

Authors：

ZENG An ¹ , SHUAI Zhifu ¹ ,  PAN Dan ² , LIN Jinzhi ³

1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, P. R. China;
2. School of Electronics and Information Engineering, Guangdong University of Technology and Education, Guangzhou 510665, P. R. China;
3. Department of Neurosurgery, The Second People’s Hospital of Guangdong Province, Guangzhou 510310, P. R. China;

Corresponding author：

PAN Dan, Email: pandan@gpnu.edu.cn

Keywords：

Alzheimer's disease; Voxel block; Multi-example learning; Multi-scale; Positional coding

DOI：

10.7507/1001-5515.202405035

Video：

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Alzheimer’s disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary diagnosis framework based on weakly supervised multi-instance learning (MIL) and multi-scale feature fusion is proposed, and the framework is designed from three aspects: within the voxel block, between voxel blocks, and high-confidence voxel blocks. First, a three-dimensional convolutional neural network was used to extract deep features within the voxel block; then the spatial correlation information between voxel blocks was captured through position encoding and attention mechanism; finally, high-confidence voxel blocks were selected and combined with multi-scale information fusion strategy to integrate key features for classification decision. The performance of the model was evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets. Experimental results showed that the proposed framework improved ACC and AUC by 3% and 4% on average compared with other mainstream frameworks in the two tasks of AD classification and mild cognitive impairment conversion classification, and could find the key voxel blocks that trigger the disease, providing an effective basis for AD auxiliary diagnosis.

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1. Jagust W. Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron, 2013, 77(2): 219-234.
2. Wisse L E M, Biessels G J, Heringa S M, et al. Hippocampal subfield volumes at 7T in early Alzheimer’s disease and normal aging. Neurobiol Aging, 2014, 35(9): 2039-2045.
3. Ashton N J, Pascoal T A, Karikari T K, et al. Plasma p-tau231: a new biomarker for incipient Alzheimer’s disease pathology. Acta Neuropathol, 2021, 141: 709-724.
4. Bron E E, Smits M, Van Der Flier W M, et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage, 2015, 111: 562-579.
5. Whitwell J L, Dickson D W, Murray M E, et al. Neuroimaging correlates of pathologically defined subtypes of Alzheimer’s disease: a case-control study. Lancet Neurol, 2012, 11(10): 868-877.
6. 杜昱峥, 曹慧, 聂永琦, 等. 深度学习在阿尔茨海默症分类诊断中的应用. 计算机工程与应用, 2023, 59(3): 49-65.
7. Ashburner J, Friston K J. Voxel-based morphometry—the methods. Neuroimage, 2000, 11(6): 805-821.
8. Pan D, Luo G, Zeng A, et al. Adaptive 3DCNN-based interpretable ensemble model for early diagnosis of Alzheimer’s disease. IEEE Trans Comput Soc Syst, 11(1): 247-266.
9. Cao P, Liu X, Yang J, et al. Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures. Comput Biol Med, 2017, 91: 21-37.
10. Zhang D, Shen D, Alzheimer’s Disease Neuroimaging Initiative. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage, 2012, 59(2): 895-907.
11. 潘丹, 邹超, 容华斌, 等. 基于遗传算法和三维卷积神经网络集成模型的阿尔茨海默症早期辅助诊断. 生物医学工程学杂志, 2021, 38(1): 47-55.
12. Qiu S, Joshi P S, Miller M I, et al. Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain, 2020, 143(6): 1920-1933.
13. Buvaneswari P R, Gayathri R. Deep learning-based segmentation in classification of Alzheimer’s disease. Arab J Sci Eng, 2021, 46(6): 5373-5383.
14. Pan D, Zeng A, Yang B, et al. Deep learning for brain MRI confirms patterned pathological progression in Alzheimer’s disease. Adv Sci, 2023, 10(6): e2204717.
15. Pan D, Zeng A, Jia L, et al. Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front Neurosci, 2020, 14: 259.
16. Zhou Z H. A brief introduction to weakly supervised learning. Nat Sci Rev, 2018, 5(1): 44-53.
17. Tong T, Wolz R, Gao Q, et al. Multiple instance learning for classification of dementia in brain MRI. Med Image Anal, 2014, 18(5): 808-818.
18. Zhu W, Sun L, Huang J, et al. Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI. IEEE Trans Med Imaging, 2021, 40(9): 2354-2366.
19. Ilse M, Tomczak J, Welling M. Attention-based deep multiple instance learning// International Conference on Machine Learning. New York: PMLR, 2018: 2127-2136.
20. Shao Z, Bian H, Chen Y, et al. Transmil: Transformer based correlated multiple instance learning for whole slide image classification. Adv Neural Inf Process Syst, 2021, 34: 2136-2147.
21. Chen L, Qiao H, Zhu F. Alzheimer’s disease diagnosis with brain structural MRI using multi view-slice attention and 3D convolution neural network. Front Aging Neurosci, 2022, 14: 871706.
22. Xu J, Zhang X, Li W, et al. Joint multi-view 2D convolutional neural networks for 3D object classification// Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. Yokohama: IJCAI, 2021: 3202-3208.
23. Zhang X, Han L, Han L, et al. sMRI-PatchNet: A novel efficient explainable patch-based deep learning network for Alzheimer’s disease diagnosis with Structural MRI. IEEE Access, 2023, 11: 108603-108616.
24. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Adv Neural Inf Process Syst, 2017, 30: 6000-6010.
25. Shamshad F, Khan S, Zamir S W, et al. Transformers in medical imaging: A survey. Med Image Anal, 2023, 88: 102802.
26. Lu M Y, Williamson D F K, Chen T Y, et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng, 2021, 5(6): 555-570.
27. Li B, Li Y, Eliceiri K W. Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 14318-14328.
28. Lian C, Liu M, Zhang J, et al. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Trans Pattern Anal Mach Intell, 2018, 42(4): 880-893.
29. 曾安, 邹超, 潘丹, 等. 基于3D卷积神经网络-感兴趣区域的阿尔茨海默症辅助诊断模型. 生物医学工程研究, 2020, 39(2): 133-138.
30. Lee J R, Kim S, Park I, et al. Relevance-CAM: Your model already knows where to look// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 14944-14953.

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