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