基于共形预测的可靠多视图学习用于超声心动图主动脉瓣狭窄分类
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography
AI总结：
针对超声心动图二维图像存在解剖信息缺失、机器学习标签缺乏不确定性的问题，提出数据驱动的RT4U方法为弱信息输入引入不确定性，结合共形预测生成高准确率保证的自适应预测集，在三类数据集上均提升了主动脉瓣狭窄分类性能。
摘要：
超声引导诊断的根本问题在于，采集到的图像通常是三维解剖结构的二维横截面，可能遗漏重要的解剖细节。这一局限给超声心动图检查带来了诸如心脏瓣膜可视化效果差或心室视图短缩等挑战。临床医生必须解读这些存在固有不确定性的图像，而这一细微差别在机器学习的独热标签中并未体现。我们提出了“不确定性再训练（Re-Training for Uncertainty, RT4U）”方法，这是一种以数据为中心的方法，用于为训练集中信息薄弱的输入引入不确定性。这种简单的方法可整合到现有的最先进主动脉瓣狭窄分类方法中，以进一步提升其准确率。当与共形预测技术结合时，RT4U可生成自适应大小的预测集，保证以高准确率包含真实类别。我们在三个不同的数据集上验证了RT4U的有效性：一个公开数据集（TMED-2）、一个私有主动脉瓣狭窄（AS）数据集，以及一个源自CIFAR-10的玩具数据集。结果表明，该方法在所有数据集上均实现了性能提升。
英文摘要：
The fundamental problem with ultrasound-guided diagnosis is that the acquired images are often 2-D cross-sections of a 3-D anatomy, potentially missing important anatomical details. This limitation leads to challenges in ultrasound echocardiography, such as poor visualization of heart valves or foreshortening of ventricles. Clinicians must interpret these images with inherent uncertainty, a nuance absent in machine learning's one-hot labels. We propose Re-Training for Uncertainty (RT4U), a data-centric method to introduce uncertainty to weakly informative inputs in the training set. This simple approach can be incorporated to existing state-of-the-art aortic stenosis classification methods to further improve their accuracy. When combined with conformal prediction techniques, RT4U can yield adaptively sized prediction sets which are guaranteed to contain the ground truth class to a high accuracy. We validate the effectiveness of RT4U on three diverse datasets: a public (TMED-2) and a private AS dataset, along with a CIFAR-10-derived toy dataset. Results show improvement on all the datasets.
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。
