GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification
The episode discusses GRC-ProbNet, a system for cardiovascular disease classification. It moves beyond single segmentation masks by using a deep ensemble to generate multiple plausible results. By incorporating uncertainty features derived from these ambiguities, the system achieves substantial performance gains and provides actionable information about its own reliability.