对比两种概念解释方法，提升糖尿病视网膜病变诊断的可解释性
Looking into Concept Explanation Methods for Diabetic Retinopathy Classification
- 采用概念激活向量与概念瓶颈模型解释AI诊断决策
- 两种方法各有优劣，需根据数据和用户偏好选择
- 为临床部署AI诊断系统提供可理解的解释支持
糖尿病视网膜病变是糖尿病常见并发症，通过眼底成像监测视网膜异常进展至关重要。由于图像需由医疗专家解读，对所有糖尿病患者进行筛查不现实。深度学习在自动分析与分级眼底图像方面表现优异，但缺乏可解释性，阻碍其临床应用。可解释人工智能方法可用于解释深度神经网络。基于概念的解释对人类更直观，但在糖尿病视网膜病变分级中尚未深入研究。本文比较了两种概念基解释技术：概念激活向量的定量测试与概念瓶颈模型，发现二者各具优势与局限，方法选择应结合可用数据及最终用户的偏好。
原文摘要 · Abstract (English)
Diabetic retinopathy is a common complication of diabetes, and monitoring the progression of retinal abnormalities using fundus imaging is crucial. Because the images must be interpreted by a medical expert, it is infeasible to screen all individuals with diabetes for diabetic retinopathy. Deep learning has shown impressive results for automatic analysis and grading of fundus images. One drawback is, however, the lack of interpretability, which hampers the implementation of such systems in the clinic. Explainable artificial intelligence methods can be applied to explain the deep neural networks. Explanations based on concepts have shown to be intuitive for humans to understand, but have not yet been explored in detail for diabetic retinopathy grading. This work investigates and compares two concept-based explanation techniques for explaining deep neural networks developed for automatic diagnosis of diabetic retinopathy: Quantitative Testing with Concept Activation Vectors and Concept Bottleneck Models. We found that both methods have strengths and weaknesses, and choice of method should take the available data and the end user's preferences into account.
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