Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views. Although numerous deep-learning models have been proposed for stenosis detection from a single angiography view, their performance heavily relies on expensive view-level annotations, which are often not readily available in hospital systems. Moreover, these models fail to capture the temporal dynamics and dependencies among multiple views, which are crucial for clinical diagnosis. To address this, we propose SegmentMIL, a transformer-based multi-view multiple-instance learning framework for patient-level stenosis classification. Trained on a real-world clinical dataset, using patient-level supervision and without any view-level annotations, SegmentMIL jointly predicts the presence of stenosis and localizes the affected anatomical region, distinguishing between the right and left coronary arteries and their respective segments. SegmentMIL obtains high performance on internal and external evaluations and outperforms both view-level models and classical MIL baselines, underscoring its potential as a clinically viable and scalable solution for coronary stenosis diagnosis. Our code is available at https://github.com/NikolaCenic/mil-stenosis.

翻译：冠状动脉狭窄是心血管疾病的主要病因，通常通过分析多个血管造影视角下的冠状动脉进行诊断。尽管已有大量深度学习模型被提出用于单视角血管造影的狭窄检测，但其性能严重依赖昂贵的视角级标注，而这类标注在医院系统中往往难以获取。此外，这些模型无法捕捉多视角间的时间动态与依赖关系，而这对于临床诊断至关重要。为此，我们提出SegmentMIL——一种基于Transformer的多视角多示例学习框架，用于患者级狭窄分类。该框架在真实世界临床数据集上训练，仅使用患者级监督且无需任何视角级标注，能够联合预测狭窄存在与否并定位受影响的解剖区域，区分左右冠状动脉及其对应分支。SegmentMIL在内部与外部评估中均取得优异性能，其表现超越视角级模型及经典多示例学习基线，彰显了其作为冠状动脉狭窄诊断临床可行且可扩展解决方案的潜力。代码已发布于https://github.com/NikolaCenic/mil-stenosis。