Toward an interpretable Alzheimer’s disease diagnostic model with regional abnormality representation via deep learning
Abstract
In this paper, we propose a novel method for magnetic resonance imaging based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis that systematically integrates voxel-based, region-based, and patch-based approaches into a unified framework. Specifically, we parcellate the brain into predefined regions based on anatomical knowledge (i.e., templates) and derive complex nonlinear relationships among voxels, whose intensities denote volumetric measurements, within each region. Unlike existing methods that use cubical or rectangular shapes, we consider the anatomical shapes of regions as atypical patches. Using complex nonlinear relationships among voxels in each region learned by deep neural networks, we extract a "regional abnormality representation." We then make a final clinical decision by integrating the regional abnormality representations over the entire brain. It is noteworthy that the regional abnormality representations allow us to interpret and understand the symptomatic observations of a subject with AD or MCI by mapping and visualizing these observations in the brain space. On the baseline MRI dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, our method achieves state-of-the-art performance for four binary classification tasks and one three-class classification task. Additionally, we conducted exhaustive experiments and analysis to validate the efficacy and potential of our method.
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Lee et al. proposed a CDSS for MRI-based Alzheimer's disease or mild cognitive impairment diagnosis that presents a 'regional abnormality map' to visualize regional abnormalities in the brain space.
“Lee et al. [119] proposed a CDSS for magnetic resonance imaging based Alzheimer's disease or mild cognitive impairment diagnosis which presents a”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [119]
Deep Neural Networks, generally a black-box technique, were explained visually with regional abnormality maps in the system proposed by Lee et al.
“Deep Neural Networks, generally a black-box technique, were explained visually with regional abnormality maps in the system proposed by Lee et al. [119].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [119]
Most studies used visualization as a key aspect to enhance explainability, either with SHAP plots, regional abnormality maps, rainbow boxes, or the attention mechanism that highlighted important words leading to a prediction.
“Most studies used visualization as a key aspect to enhance explainability, either with SHAP plots [118,120], regional abnormality maps [119], rainbow boxes [123,126], or the attention mechanism that highlighted the important words that lead to a prediction [122].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [119]
The remaining CDSS implemented model-agnostic post-hoc methods for local explanations.
“The remaining CDSS implemented model-agnostic post-hoc methods for local explanations [119,123].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [119]
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“The method was validated on a weighted MRI dataset and provided better performance over Regional Mean Volume (RMV), and Hierarchical Feature Fusion (HFF) methods using the four metrics (accuracy, sensitivity, specificity, and AUC) for the binary and multiclass classification [145].”
MethodInfluentialDeep learning for Alzheimer's disease diagnosis: A survey
Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102332 · Cited by 194 · see citing works
Interpretable machine learning for dementia: a systematic review
Martin, S. A., Townend, F. J., Barkhof, F., & Cole, J. H. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12948 · Cited by 149 · see citing works
- Authors
- Lee, E., Choi, J.-S., Kim, M., & Suk, H.-I.
- Affiliations
- Korea University (KR)
- University of North Carolina at Greensboro (US)
- Corresponding author
- Suk, Heung-Il
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 202
- Pages
- 116113
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2969595880
- Cited by
- 61
- References
- 59
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 12 · 2023: 18 · 2022: 10 · 2021: 10 · 2020: 2
- FWCI
- 4.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Institute for Information and Communications Technology Promotion (2017-0-01779)
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea (NRF - 2016M3A9E9941946)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- Alzheimer's disease diagnosis, mild cognitive impairment, MRI diagnosis, explainable deep learning, brain parcellation, ADNI cohort
- MeSH terms
- Deep Learning, Alzheimer Disease, Brain, Brain Mapping, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Cognitive Dysfunction
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