Patch-based interpretable deep learning framework for Alzheimer’s disease diagnosis using multimodal data
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CNNs applied to PET scans may achieve high area-under-the-curve scores, but their saliency maps frequently fail to align with known pathological regions, reducing clinician trust.
“Convolutional neural networks applied to PET scans may achieve high area-under-the-curve scores, but their saliency maps frequently fail to align with known pathological regions, reducing clinician trust.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [289]
Benchmarking studies indicate that outputs of XAI techniques can be inconsistent and model-dependent.
“Techniques such as attention layers, SHAP, and patch-based visualizations aim to localize voxel- or gene-level contribution, yet benchmarking studies indicate that their outputs can be inconsistent and model-dependent256,282,289.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [289]
Cites
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537 · see citing works
Can we open the black box of AI?
Castelvecchi, D. (2016) · Nature · doi:10.1038/538020a · Cited by 1,773 · see citing works
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968 · see citing works
Multimodal deep learning for Alzheimer's disease dementia assessment
Qiu, S., Miller, M. I., Joshi, P. S., Lee, J. C., Xue, C., Ni, Y., Wang, Y., De Anda-Duran, I., Hwang, P. H., Cramer, J. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31037-5 · Cited by 420 · see citing works
An effective multimodal image fusion method using MRI and PET for Alzheimer’s disease diagnosis
Song, J., Zheng, J., Li, P., Lu, X., Zhu, G., & Shen, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.637386 · Cited by 178 · see citing works
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Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
A Review of Multimodal Fusion–Based Deep Learning for Alzheimer’s Disease
Zhang, R., Sheng, J., Zhang, Q., Wang, J., & Wang, B. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.04.035 · Cited by 32 · see citing works
- Authors
- Zhang, H., Ni, M., Yang, Y., Xie, F., Wang, W., He, Y., Chen, W., & Chen, Z.
- Affiliations
- University of Science and Technology of China (CN)
- Fudan University (CN)
- Corresponding author
- Chen, Weiqiang
- Chen, Zhi
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2025-02
- Volume
- 100
- Pages
- 107085
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W4403562985
- Cited by
- 34
- References
- 5
- Citations by year
- 2026: 19 · 2025: 14 · 2024: 1
- FWCI
- 9.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Biomedical Signal Processing and Control (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease diagnosis, mild cognitive impairment, multimodal fusion, explainable deep learning, magnetic resonance imaging, PET, Mini-Mental State Examination, early diagnosis
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