Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of these views show the aortic valve. To automate screening for AS, deep networks must learn to mimic a human expert's ability to identify views of the aortic valve then aggregate across these relevant images to produce a study-level diagnosis. We find previous approaches to AS detection yield insufficient accuracy due to relying on inflexible averages across images. We further find that off-the-shelf attention-based multiple instance learning (MIL) performs poorly. We contribute a new end-to-end MIL approach with two key methodological innovations. First, a supervised attention technique guides the learned attention mechanism to favor relevant views. Second, a novel self-supervised pretraining strategy applies contrastive learning on the representation of the whole study instead of individual images as commonly done in prior literature. Experiments on an open-access dataset and an external validation set show that our approach yields higher accuracy while reducing model size.

翻译：主动脉瓣狭窄（AS）是一种退行性瓣膜疾病，导致显著的发病率和死亡率。该疾病存在诊断不足和治疗不足的问题。临床实践中，AS的诊断依赖于专家对经胸超声心动图的解读，该检查会生成数十张心脏超声图像，但只有部分视图能显示主动脉瓣。为自动化AS筛查，深度网络必须学习模仿人类专家识别主动脉瓣视图的能力，然后聚合这些相关图像以产生研究级诊断。我们发现，现有AS检测方法因依赖图像间的固定平均而精度不足；同时，现成的基于注意力的多实例学习（MIL）表现不佳。我们提出了一种全新的端到端MIL方法，包含两项关键方法创新：第一，有监督注意力技术引导学习到的注意力机制优先关注相关视图；第二，一种新颖的自监督预训练策略，在整体研究的表征上应用对比学习，而非过往文献中常用的单个图像。在公开数据集和外部验证集上的实验表明，我们的方法在降低模型规模的同时实现了更高的准确率。