用典型病例辅助诊断口腔溃疡，让模型决策更透明可信。
Interpretable Machine Learning for Oral Lesion Diagnosis through Prototypical Instances Identification
- 通过挑选典型病例作为原型，解释模型判断依据。
- 在口腔溃疡检测任务中达到良好准确率，优于传统方法。
- 适合需要可解释性的医疗AI场景，如临床辅助诊断。
医疗决策过程复杂且具挑战性。机器学习工具虽有潜力辅助，但多数现有方法依赖难以理解的复杂模型。为此，本文提出使用可解释的原型选择模型PivotTree，解决从口腔腔内图像中识别癌变、复发性及创伤性溃疡病变的问题。该方法模拟人类通过记忆中的典型病例进行判断的机制，选取代表性样本作为决策依据。实验表明，该方法在性能上表现优异，并通过定性和定量分析验证了其选出的典型病例与专家标注的真值原型高度一致，为临床决策提供了可解释的支持。
原文摘要 · Abstract (English)
Decision-making processes in healthcare can be highly complex and challenging. Machine Learning tools offer significant potential to assist in these processes. However, many current methodologies rely on complex models that are not easily interpretable by experts. This underscores the need to develop interpretable models that can provide meaningful support in clinical decision-making. When approaching such tasks, humans typically compare the situation at hand to a few key examples and representative cases imprinted in their memory. Using an approach which selects such exemplary cases and grounds its predictions on them could contribute to obtaining high-performing interpretable solutions to such problems. To this end, we evaluate PivotTree, an interpretable prototype selection model, on an oral lesion detection problem, specifically trying to detect the presence of neoplastic, aphthous and traumatic ulcerated lesions from oral cavity images. We demonstrate the efficacy of using such method in terms of performance and offer a qualitative and quantitative comparison between exemplary cases and ground-truth prototypes selected by experts.
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