Deep Joint Learning of Pathological Region Localization and Alzheimer's Disease Diagnosis

• URL: http://arxiv.org/abs/2108.04555v1
• Date: Tue, 10 Aug 2021 10:06:54 GMT
• Title: Deep Joint Learning of Pathological Region Localization and Alzheimer's Disease Diagnosis
• Authors: Changhyun Park and Heung-Il Suk
• Abstract summary: BrainBagNet is a framework for jointly learning pathological region localization and Alzheimer's disease diagnosis. The proposed method represents the patch-level response from whole-brain MRI scans and discriminative brain-region from position information. In five-fold cross-validation, the classification performance of the proposed method outperformed that of the state-of-the-art methods in both AD diagnosis and mild cognitive impairment prediction tasks.
• Score: 4.5484714814315685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The identification of Alzheimer's disease (AD) and its early stages using structural magnetic resonance imaging (MRI) has been attracting the attention of researchers. Various data-driven approaches have been introduced to capture subtle and local morphological changes of the brain accompanied by the disease progression. One of the typical approaches for capturing subtle changes is patch-level feature representation. However, the predetermined regions to extract patches can limit classification performance by interrupting the exploration of potential biomarkers. In addition, the existing patch-level analyses have difficulty explaining their decision-making. To address these problems, we propose the BrainBagNet with a position-based gate (PG-BrainBagNet), a framework for jointly learning pathological region localization and AD diagnosis in an end-to-end manner. In advance, as all scans are aligned to a template in image processing, the position of brain images can be represented through the 3D Cartesian space shared by the overall MRI scans. The proposed method represents the patch-level response from whole-brain MRI scans and discriminative brain-region from position information. Based on the outcomes, the patch-level class evidence is calculated, and then the image-level prediction is inferred by a transparent aggregation. The proposed models were evaluated on the ADNI datasets. In five-fold cross-validation, the classification performance of the proposed method outperformed that of the state-of-the-art methods in both AD diagnosis (AD vs. normal control) and mild cognitive impairment (MCI) conversion prediction (progressive MCI vs. stable MCI) tasks. In addition, changes in the identified discriminant regions and patch-level class evidence according to the patch size used for model training are presented and analyzed.

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