基于心脏影像的二尖瓣反流自动检测机器学习方法
Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging
AI总结：
针对二尖瓣反流传统诊断成本高、依赖专业人员的筛查难题，研究提出半监督模型CUSSP，结合对比学习与专用分类器实现自动化MR分类，在测试集上取得0.69的F1与0.88的ROC-AUC，为该任务设立首个基准。
摘要：
二尖瓣反流（MR）是一种可能造成致命后果的心脏瓣膜疾病，唯有通过及时诊断与治疗才能阻止病情恶化。传统诊断方法成本高昂、耗费人力且需要临床专业知识，为MR筛查带来了阻碍。为攻克这一难题，我们提出了一种用于MR分类的新型半监督模型，名为CUSSP。CUSSP以心脏四腔心视图的心脏影像切片为输入，结合标准计算机视觉技术与对比模型从大量无标注数据中学习，并搭配专用分类器，构建了首个自动化MR分类系统。在包含179个标注序列（154个非MR序列、25个MR序列）的测试集上评估，CUSSP取得了0.69的F1分数与0.88的ROC-AUC分数，为这一新任务设立了首个基准结果。
英文摘要：
Mitral regurgitation (MR) is a heart valve disease with potentially fatal consequences that can only be forestalled through timely diagnosis and treatment. Traditional diagnosis methods are expensive, labor-intensive and require clinical expertise, posing a barrier to screening for MR. To overcome this impediment, we propose a new semi-supervised model for MR classification called CUSSP. CUSSP operates on cardiac imaging slices of the 4-chamber view of the heart. It uses standard computer vision techniques and contrastive models to learn from large amounts of unlabeled data, in conjunction with specialized classifiers to establish the first ever automated MR classification system. Evaluated on a test set of 179 labeled -- 154 non-MR and 25 MR -- sequences, CUSSP attains an F1 score of 0.69 and a ROC-AUC score of 0.88, setting the first benchmark result for this new task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
