用分割模型提升医学影像诊断解释性，让AI关注医生关心的区域。
SegX: Improving Interpretability of Clinical Image Diagnosis with Segmentation-based Enhancement
- 基于分割结果增强解释图，对齐临床关注区域。
- 在皮肤和胸部X光数据集上显著提升解释一致性。
- 新增置信度评分，可反映模型预测可靠性，适合临床部署。
基于深度学习的医学图像分析因缺乏可解释性面临重大障碍。传统可解释AI（XAI）技术如Grad-CAM和SHAP常聚焦于非临床关注区域。为此，我们提出基于分割的解释方法SegX，一种即插即用方案，通过利用分割模型的力量，使模型的解释图与临床相关区域对齐，从而提升可解释性。此外，我们引入基于分割的不确定性评估（SegU），通过测量解释图与临床显著区域之间的‘距离’来量化预测模型的不确定性。在皮肤镜和胸部X光数据集上的实验表明，SegX在多种死亡率场景下均能一致提升解释性；而SegU提供的置信度评分能可靠反映模型预测的正确性。该方法为医学图像诊断提供了无需修改模型的可解释性增强，助力临床决策中可信AI的应用。
原文摘要 · Abstract (English)
Deep learning-based medical image analysis faces a significant barrier due to the lack of interpretability. Conventional explainable AI (XAI) techniques, such as Grad-CAM and SHAP, often highlight regions outside clinical interests. To address this issue, we propose Segmentation-based Explanation (SegX), a plug-and-play approach that enhances interpretability by aligning the model's explanation map with clinically relevant areas leveraging the power of segmentation models. Furthermore, we introduce Segmentation-based Uncertainty Assessment (SegU), a method to quantify the uncertainty of the prediction model by measuring the 'distance' between interpretation maps and clinically significant regions. Our experiments on dermoscopic and chest X-ray datasets show that SegX improves interpretability consistently across mortalities, and the certainty score provided by SegU reliably reflects the correctness of the model's predictions. Our approach offers a model-agnostic enhancement to medical image diagnosis towards reliable and interpretable AI in clinical decision-making.
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