通过剪枝皮肤色调无关特征，提升皮肤病变分类公平性。
Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning
- 基于VGG和Vision Transformer的特征图偏度分析，识别并剪除与肤色相关的冗余通道。
- 在不依赖传统统计方法的前提下，显著降低模型对不同肤色的偏差。
- 兼顾计算效率与公平性，适合医疗场景部署。
深度学习虽大幅提升了皮肤病变分类的准确率，支持医疗诊断并促进医疗公平，但肤色相关偏见仍可能影响诊断结果。由于肤色分类困难、计算成本高且公平性验证复杂，实现诊断公平面临挑战。本文提出一种面向皮肤病变分类的公平性算法，通过计算VGG网络卷积层及Vision Transformer的局部块与注意力头的特征图偏度，识别并去除与肤色相关的冗余通道，聚焦病变区域。该方法降低计算开销，无需依赖传统统计手段即可缓解偏见，同时可能减小模型规模，在保持公平性的同时提升实际应用可行性。
原文摘要 · Abstract (English)
Recent advances in deep learning have significantly improved the accuracy of skin lesion classification models, supporting medical diagnoses and promoting equitable healthcare. However, concerns remain about potential biases related to skin color, which can impact diagnostic outcomes. Ensuring fairness is challenging due to difficulties in classifying skin tones, high computational demands, and the complexity of objectively verifying fairness. To address these challenges, we propose a fairness algorithm for skin lesion classification that overcomes the challenges associated with achieving diagnostic fairness across varying skin tones. By calculating the skewness of the feature map in the convolution layer of the VGG (Visual Geometry Group) network and the patches and the heads of the Vision Transformer, our method reduces unnecessary channels related to skin tone, focusing instead on the lesion area. This approach lowers computational costs and mitigates bias without relying on conventional statistical methods. It potentially reduces model size while maintaining fairness, making it more practical for real-world applications.
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