Unveiling hidden risks: A Holistically-Driven Weak Supervision framework for ultra-short-term ACS prediction using...

This paper proposes MH-STR, a novel end-to-end framework for predicting the three-month risk of Acute Coronary Syndrome (ACS) from Coronary CT Angiography (CCTA) images. The model combines hybrid attention mechanisms with convolutional networks to capture subtle and irregular lesion patterns that are difficult to detect visually. A stage-wise transfer learning strategy helps distill general features and transfer vascular-specific knowledge. To reconcile feature scale mismatches in the dual-branch architecture, we introduce a wavelet-based multi-scale fusion module for effective integration across scales. Experiments show that MH-STR achieves an AUC of 0.834, an F1 score of 0.82, and a precision of 0.92, outperforming existing methods and highlighting its potential for improving ACS risk prediction.