Coronary artery disease stands as one of the primary contributors to global mortality rates. The automated identification of coronary artery stenosis from X-ray images plays a critical role in the diagnostic process for coronary heart disease. This task is challenging due to the complex structure of coronary arteries, intrinsic noise in X-ray images, and the fact that stenotic coronary arteries appear narrow and blurred in X-ray angiographies. This study employs five different variants of the Mamba-based model and one variant of the Swin Transformer-based model, primarily based on the U-Net architecture, for the localization of stenosis in Coronary artery disease. Our best results showed an F1 score of 68.79% for the U-Mamba BOT model, representing an 11.8% improvement over the semi-supervised approach.

翻译：冠状动脉疾病是全球死亡率的主要诱因之一。从X射线图像中自动识别冠状动脉狭窄在冠心病的诊断过程中起着关键作用。由于冠状动脉结构复杂、X射线图像存在固有噪声，且狭窄的冠状动脉在X射线血管造影中呈现狭窄模糊状态，该任务具有挑战性。本研究采用五种基于Mamba的模型变体和一种基于Swin Transformer的模型变体（主要基于U-Net架构）进行冠状动脉疾病狭窄定位。我们的最佳结果显示，U-Mamba BOT模型的F1分数达到68.79%，相较于半监督方法提升了11.8%。