Due to the high stakes in medical decision-making, there is a compelling
demand for interpretable deep learning methods in medical image analysis.
Concept Bottleneck Models (CBM) have emerged as an active interpretable
framework incorporating human-interpretable concepts into decision-making.
However, their concept predictions may lack reliability when applied to
clinical diagnosis, impeding concept explanations' quality. To address this, we
propose an evidential Concept Embedding Model (evi-CEM), which employs
evidential learning to model the concept uncertainty. Additionally, we offer to
leverage the concept uncertainty to rectify concept misalignments that arise
when training CBMs using vision-language models without complete concept
supervision. With the proposed methods, we can enhance concept explanations'
reliability for both supervised and label-efficient settings. Furthermore, we
introduce concept uncertainty for effective test-time intervention. Our
evaluation demonstrates that evi-CEM achieves superior performance in terms of
concept prediction, and the proposed concept rectification effectively
mitigates concept misalignments for label-efficient training. Our code is
available at this https URL

由于医疗决策的重要性，对于医学图像分析中的可解释深度学习方法有着强烈的需求。本文提出了一种基于证据学习的概念嵌入模型 (ev-CEM)，用于建模概念的不确定性，并利用其来纠正在没有完全概念监督下使用视觉 - 语言模型训练概念瓶颈模型时出现的概念错位。通过使用所提出的方法，我们可以增强监督和高效标签设置下的概念解释的可靠性。此外，我们还引入了概念的不确定性以实现有效的测试时干预。我们的评估结果表明，ev-CEM 在概念预测方面具有出色的性能，并且所提出的概念纠正方法有效地减少了高效标签训练中的概念错位。我们的代码可在此 https URL 获取。