Per-structure KL-divergence uncertainty features from a five-model Anatomix ensemble improve ASOCA CVD AUROC to 92.92% over deterministic GRC-Net's 91.25%.
In:International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, (2023).https://link.springer.com/ chapter/10.1007/978-3-031-44336-7_1
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GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification
Per-structure KL-divergence uncertainty features from a five-model Anatomix ensemble improve ASOCA CVD AUROC to 92.92% over deterministic GRC-Net's 91.25%.