TopoCAM: ROI-Driven Topological Signatures in 3D Medical Imaging

Brighton Nuwagira, Philmore Koung, Baris Coskunuzer

Keywords: 3D medical imaging, deep learning, explainable AI, Grad-CAM, topological data analysis, persistent homology, clinical decision support

TL;DR: We combine Grad-CAM–guided segmentation with topological descriptors to improve 3D medical image classification, achieving higher accuracy, robustness, and interpretability than CNN and Transformer baselines.

Track: Proceedings

Abstract: Accurate classification of 3D medical images is challenging due to the high dimensionality of volumetric data and the scarcity of well-annotated clinical datasets. We propose a hybrid framework that couples explainable deep learning with topological data analysis (TDA). First, we compute layer-weighted Grad-CAM across multiple network layers, upsample and normalize the maps to the input grid, and threshold them to produce a binary region-of-interest (ROI) mask. We then apply this mask to the input volume to obtain a segmented image that suppresses irrelevant anatomy while preserving clinically salient structures. Within these attention-derived ROIs and segmented images, we compute cubical persistent homology to derive compact topological descriptors that capture diagnostically meaningful features. Across both 3D volumes and 2D medical imaging benchmarks, this segmentation-guided TDA pipeline surpasses strong 3D CNN and Transformer baselines, yielding higher accuracy and improved robustness in limited-data settings while providing localized, interpretable evidence for clinical decision support.

General Area: Models and Methods

Specific Subject Areas: Medical Imaging

Data And Code Availability: Yes

Ethics Board Approval: No

Entered Conflicts: I confirm the above

Anonymity: I confirm the above

Submission Number: 35