论文标题
迈向值得信赖的医疗保健AI：基于注意的胸部射线照相筛查的基于注意力的功能学习
Towards Trustworthy Healthcare AI: Attention-Based Feature Learning for COVID-19 Screening With Chest Radiography
论文作者
论文摘要
建立具有可信赖性的AI模型非常重要，尤其是在医疗保健等受监管的地区。在解决COVID-19时，以前的工作将卷积神经网络用作骨干建筑，该骨干结构已证明很容易在做出决策方面过度谨慎和过度自信，使它们不那么值得信赖 - 在医学成像的背景下是一个关键缺陷。在这项研究中，我们提出了一种使用视觉变形金刚的功能学习方法，该方法使用基于注意力的机制，并研究变形金刚作为医学成像的新骨干结构的表示能力。通过对COVID-19胸部X光片进行分类的任务，我们研究了概括能力是否仅受益于视觉变形金刚的建筑进步。通过使用“信任评分”计算和视觉解释性技术，对模型的可信度进行了定量和定性评估。我们得出的结论是，基于注意力的特征学习方法在建立可信赖的深度学习模型方面有希望。
Building AI models with trustworthiness is important especially in regulated areas such as healthcare. In tackling COVID-19, previous work uses convolutional neural networks as the backbone architecture, which has shown to be prone to over-caution and overconfidence in making decisions, rendering them less trustworthy -- a crucial flaw in the context of medical imaging. In this study, we propose a feature learning approach using Vision Transformers, which use an attention-based mechanism, and examine the representation learning capability of Transformers as a new backbone architecture for medical imaging. Through the task of classifying COVID-19 chest radiographs, we investigate into whether generalization capabilities benefit solely from Vision Transformers' architectural advances. Quantitative and qualitative evaluations are conducted on the trustworthiness of the models, through the use of "trust score" computation and a visual explainability technique. We conclude that the attention-based feature learning approach is promising in building trustworthy deep learning models for healthcare.
