GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification
2026-07-11 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors improved a method for detecting heart disease from CT images by making the image segmentation step more flexible and uncertain, instead of just fixed. They used multiple segmentation results to capture uncertainty and studied how this uncertainty related to errors and disease classification. Their new approach, GRC-ProbNet, showed better performance in identifying cardiovascular diseases than the previous fixed method. They also found that the best uncertainty measure for segmentation quality is not always the best for disease classification.
cardiovascular diseasecomputed tomographyimage segmentationdeep learninguncertainty quantificationradiomicsensemble methodsclassificationAUROCregistration
Authors
Yash Shah, Omar Todd, Philipp Seeböck, Georg Langs, Ben Glocker, Raghav Mehta
Abstract
The automatic detection and classification of cardiovascular disease (CVD) from computed tomography (CT) images plays an important role in clinical practice. Recently, a hybrid pipeline (GRC-Net) for CVD classification was proposed, which leverages a deep-learning-based segmentation and registration method to extract radiomic and geometric features. However, GRC-Net relies on a deterministic segmentation mask, without considering the inherent ambiguity associated with cardiac anatomy. In this paper, we propose GRC-ProbNet, which takes advantage of a deep ensemble to produce multiple segmentation masks for a given input. From these masks, we extract multiple uncertainty features. We analyze these uncertainty features for both their correlation with segmentation error and their propagation effects on downstream CVD classification performance. Our experiments on the publicly available MM-WHS and ASOCA datasets show that the uncertainty measure that best reflects segmentation quality is not necessarily the one that provides the strongest signal for downstream CVD classification. Overall, our results demonstrate that GRC-ProbNet utilizing uncertainty features substantially improves CVD classification AUROC (92.92\) compared to the baseline GRC-Net model (91.25%). Our code is publicly available: https://github.com/biomedia-mira/GRC-ProbNet.