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

Brighton Nuwagira, Philmore Koung, Baris Coskunuzer
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:41-54, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-nuwagira26a, title = {{TopoCAM}: {ROI}-Driven Topological Signatures in {3D} Medical Imaging}, author = {Nuwagira, Brighton and Koung, Philmore and Coskunuzer, Baris}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {41--54}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/nuwagira26a/nuwagira26a.pdf}, url = {https://proceedings.mlr.press/v297/nuwagira26a.html}, 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.} }
Endnote
%0 Conference Paper %T TopoCAM: ROI-Driven Topological Signatures in 3D Medical Imaging %A Brighton Nuwagira %A Philmore Koung %A Baris Coskunuzer %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-nuwagira26a %I PMLR %P 41--54 %U https://proceedings.mlr.press/v297/nuwagira26a.html %V 297 %X 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.
APA
Nuwagira, B., Koung, P. & Coskunuzer, B.. (2026). TopoCAM: ROI-Driven Topological Signatures in 3D Medical Imaging. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:41-54 Available from https://proceedings.mlr.press/v297/nuwagira26a.html.

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