用真实临床数据训练，无需视图标注即可多视角诊断冠状动脉狭窄。
Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data
- 基于Transformer的多实例学习框架，融合多视角影像信息。
- 在内外部测试中表现优异，优于传统方法和单视图模型。
- 适合医院部署，可实现患者级诊断且无需昂贵标注。
冠状动脉狭窄是心血管疾病的主要原因，需通过多角度造影图像分析诊断。尽管已有大量深度学习模型用于单视角狭窄检测，但其性能高度依赖昂贵的视图级标注，而这类标注在实际医院系统中难以获取。此外，现有模型未能捕捉多视角间的时序动态与依赖关系，影响临床诊断准确性。为此，我们提出SegmentMIL：一种基于Transformer的多视角多实例学习框架，用于患者级狭窄分类。该模型在真实临床数据上训练，仅需患者级标签，无需任何视图级标注，能够联合预测狭窄存在并定位受累解剖区域，区分左右冠状动脉及其各节段。在内部与外部评估中，SegmentMIL均表现出色，显著优于视图级模型及经典MIL基线，展现出作为临床可行、可扩展的冠状动脉狭窄诊断方案的巨大潜力。代码已开源。
原文摘要 · Abstract (English)
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.
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