SegmentMIL

Transformer-based multi-view multiple-instance learning (MIL) framework for patient-level coronary stenosis classification from multi-view invasive coronary angiography. Rather than requiring expensive view-level stenosis annotations, SegmentMIL is trained end-to-end on real-world clinical data using only patient-level labels already present in hospital systems, and jointly predicts stenosis presence while localizing the affected artery (left/right) and segment. It captures temporal dynamics and dependencies across the multiple angiographic views per patient (which prior view-level models ignore), and outperforms both single-view models and classical MIL baselines on internal and external clinical evaluations.