模型关注病变区域越准，皮肤癌分类越准。
The Impact of Lesion Focus on the Performance of AI-Based Melanoma Classification
- 用遮蔽、框选和迁移学习分析模型注意力
- 病变聚焦越高，准确率、召回率与F1值越好
- 适合医疗AI可解释性研究者参考
黑色素瘤是致死率最高的皮肤癌类型，早期精准检测可显著改善患者预后。尽管卷积神经网络（CNN）在自动化黑色素瘤分类中展现出巨大潜力，但其诊断可靠性仍受限于对病灶区域的关注不一致。本研究通过遮蔽图像、边界框检测和迁移学习，分析病灶注意力与诊断性能的关系。采用多种可解释性与敏感性分析方法，探究模型注意力与病灶区域的对齐程度，及其与精确率、召回率和F1分数的相关性。结果表明，更聚焦于病灶区域的模型表现出更优的诊断性能，提示可解释人工智能在医学诊断中的潜力。本研究为未来开发更精准、可信的黑色素瘤分类模型奠定了基础。
原文摘要 · Abstract (English)
Melanoma is the most lethal subtype of skin cancer, and early and accurate detection of this disease can greatly improve patients' outcomes. Although machine learning models, especially convolutional neural networks (CNNs), have shown great potential in automating melanoma classification, their diagnostic reliability still suffers due to inconsistent focus on lesion areas. In this study, we analyze the relationship between lesion attention and diagnostic performance, involving masked images, bounding box detection, and transfer learning. We used multiple explainability and sensitivity analysis approaches to investigate how well models aligned their attention with lesion areas and how this alignment correlated with precision, recall, and F1-score. Results showed that models with a higher focus on lesion areas achieved better diagnostic performance, suggesting the potential of interpretable AI in medical diagnostics. This study provides a foundation for developing more accurate and trustworthy melanoma classification models in the future.
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