用概率化激活图提升皮肤病变分类可信度，防误诊。
Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML
- 全局概率化分析所有类别激活图，像素级可视化诊断过程
- 在ISIC数据集上提升模型可解释性，降低误诊风险
- 适合临床医生和患者，增强AI辅助诊断的安全性
近期皮肤病变分类模型的精度已显著提升，部分模型甚至超过皮肤科医生。然而，医疗实践中对AI的信任仍是难题。除了高准确率，可信赖、可解释的诊断至关重要。现有可解释方法存在可靠性问题：LIME类方法不一致，CAM类方法未考虑所有类别。为此，我们提出全局类别激活概率图评估（Global Class Activation Probabilistic Map Evaluation），从概率角度在像素级别分析所有类别的激活图，统一可视化诊断过程，有助于降低误诊风险。此外，通过SafeML应用实现假诊断检测并适时向医生和患者发出警告，进一步提升诊断可靠性和患者安全。我们在ISIC数据集上使用MobileNetV2和视觉变换器（Vision Transformers）验证了该方法。
原文摘要 · Abstract (English)
Recent advancements in skin lesion classification models have significantly improved accuracy, with some models even surpassing dermatologists' diagnostic performance. However, in medical practice, distrust in AI models remains a challenge. Beyond high accuracy, trustworthy, explainable diagnoses are essential. Existing explainability methods have reliability issues, with LIME-based methods suffering from inconsistency, while CAM-based methods failing to consider all classes. To address these limitations, we propose Global Class Activation Probabilistic Map Evaluation, a method that analyses all classes' activation probability maps probabilistically and at a pixel level. By visualizing the diagnostic process in a unified manner, it helps reduce the risk of misdiagnosis. Furthermore, the application of SafeML enhances the detection of false diagnoses and issues warnings to doctors and patients as needed, improving diagnostic reliability and ultimately patient safety. We evaluated our method using the ISIC datasets with MobileNetV2 and Vision Transformers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。