Deep joint learning of pathological region localization and Alzheimer's disease diagnosis.

ABSTRACT: The identification of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) has been studied based on the subtle morphological changes in the brain. One of the typical approaches is a deep learning-based patch-level feature representation. For this approach, however, the predetermined patches before learning the diagnostic model can limit classification performance. To mitigate this problem, we propose the BrainBagNet with a position-based gate (PG), which applies position information of brain images represented through the 3D coordinates. Our proposed method represents the patch-level class evidence based on both MR scan and position information for image-level prediction. To validate the effectiveness of our proposed framework, we conducted comprehensive experiments

SUBMITTER: Park C 

PROVIDER: S-EPMC10356790 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature