Abstract
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.
Abstract (translated)
黑色素瘤是皮肤癌中最致命的一种，该疾病的早期和准确检测能够极大地改善患者的预后。尽管机器学习模型（尤其是卷积神经网络CNN）在自动化黑色素瘤分类方面展现出巨大潜力，但由于对病变区域的关注度不一致，其诊断可靠性仍然存在不足。在这项研究中，我们分析了病变关注与诊断性能之间的关系，涉及遮罩图像、边界框检测和迁移学习等方法。通过使用多种解释性和敏感性分析方法，我们调查了模型如何与其在病变区域上的注意力对齐，并探讨这种对齐程度如何与精度、召回率和F1分数相关联。结果显示，关注病变区域更密切的模型实现了更好的诊断性能，这表明可解释的人工智能技术在未来医学诊断中的潜力。这项研究为开发更加准确和可信的黑色素瘤分类模型奠定了基础。
URL
https://arxiv.org/abs/2601.00355
