ProtoASNet：用于超声心动图中主动脉瓣狭窄分类的动态原型与内在可解释及不确定性感知方法
ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography
AI总结：
提出原型网络ProtoASNet，通过时空原型相似性和弃权损失实现可解释且不确定性感知的主动脉瓣狭窄检测，在私有与TMED-2数据集上准确率达80.0%和79.7%，优于现有方法。
摘要：
主动脉瓣狭窄（AS）是一种常见的心脏瓣膜疾病，需要准确及时的诊断以进行适当治疗。目前大多数自动AS严重程度检测方法依赖于可信度较低的黑盒模型，这阻碍了其临床应用。为解决这一问题，我们提出了ProtoASNet，这是一种原型网络，可直接从B型超声心动图视频中检测AS，同时基于输入与学习到的时空原型之间的相似性做出可解释的预测。该方法提供了与临床相关的支持性证据，因为原型通常会突出显示主动脉瓣小叶钙化和活动受限等标志物。此外，ProtoASNet利用弃权损失来估计偶然不确定性，其方式是定义一组原型来捕捉观测数据中的模糊性和信息不足。这提供了一个可靠的系统，能够检测并解释其何时可能失效。我们在私有数据集和公开的TMED-2数据集上评估了ProtoASNet，其准确率分别达到80.0%和79.7%，优于现有的最先进方法。此外，ProtoASNet为每次预测提供了可解释性和不确定性度量，这能提高透明度并促进深度网络的交互式使用以辅助临床决策。源代码可在：https://github.com/hooman007/ProtoASNet 获取。
英文摘要：
Aortic stenosis (AS) is a common heart valve disease that requires accurate and timely diagnosis for appropriate treatment. Most current automatic AS severity detection methods rely on black-box models with a low level of trustworthiness, which hinders clinical adoption. To address this issue, we propose ProtoASNet, a prototypical network that directly detects AS from B-mode echocardiography videos, while making interpretable predictions based on the similarity between the input and learned spatio-temporal prototypes. This approach provides supporting evidence that is clinically relevant, as the prototypes typically highlight markers such as calcification and restricted movement of aortic valve leaflets. Moreover, ProtoASNet utilizes abstention loss to estimate aleatoric uncertainty by defining a set of prototypes that capture ambiguity and insufficient information in the observed data. This provides a reliable system that can detect and explain when it may fail. We evaluate ProtoASNet on a private dataset and the publicly available TMED-2 dataset, where it outperforms existing state-of-the-art methods with an accuracy of 80.0% and 79.7%, respectively. Furthermore, ProtoASNet provides interpretability and an uncertainty measure for each prediction, which can improve transparency and facilitate the interactive usage of deep networks to aid clinical decision-making. Our source code is available at: https://github.com/hooman007/ProtoASNet.
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。
