SliceMap: a binary classification-driven 2D pipeline for detecting discriminative candidate regions in brain MRI

Identifying spatially localized neuroanatomical signals from brain magnetic resonance imaging (MRI) is central to many clinical and scientific questions, including medical diagnosis and the investigation of disease mechanisms. However, reliably detecting such subtle and spatially localized signals from high-dimensional MRI data remains challenging. In our study, we propose a performance-guided two-dimensional (2D) slice-based pipeline for identifying candidate spatial regions in brain MRI. The pipeline employs efficient 2D convolutional neural networks trained on plane-specific MRI slices, using binary classification as a modeling task to evaluate slice-level performance. The best-performing slice from each anatomical plane is then subjected to occlusion-based attribution analysis, and the resulting maps are jointly examined to localize a candidate three-dimensional brain region. We demonstrate the proposed pipeline using sex classification as a controlled testbed, identifying a spatially localized candidate region corresponding to the anterior cingulate cortex and adjacent medial structures including portions of the corpus callosum genu, consistent with prior neuroanatomical findings on sex differences in these regions.