用注意力机制引导模型关注病灶，提升皮肤癌诊断公平性
Attention-Guided Fair AI Modeling for Skin Cancer Diagnosis
- 通过临床知识引导注意力聚焦病灶区域，模仿医生诊断习惯
- 在两个大规模数据集上显著降低性别偏差，同时保持高诊断准确率
- 适合关注AI医疗公平性的研究者与临床医生合作参考
人工智能在皮肤病学中展现出巨大潜力，可实现精准且无创的皮肤癌诊断。尽管已有大量研究关注肤色相关的偏见，但皮肤科AI中的性别偏见仍缺乏充分探索，导致医疗不公并加剧现有性别差异。本研究提出LesionAttn，一种融合临床知识的公平性感知算法，通过引导模型注意力聚焦病变区域，模拟临床医生的诊断重点。结合帕累托前沿优化进行双目标模型选择，有效平衡公平性与预测准确性。在两个大规模皮肤病数据集上的验证表明，LesionAttn显著缓解了性别偏见，同时保持优异的诊断性能，优于现有偏见缓解算法。研究凸显了将临床知识嵌入AI开发的潜力，有助于提升模型性能与公平性，并推动临床医生与AI开发者之间的跨学科协作。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has shown remarkable promise in dermatology, offering accurate and non-invasive diagnosis of skin cancer. While extensive research has addressed skin tone-related bias, gender bias in dermatologic AI remains underexplored, leading to unequal care and reinforcing existing gender disparities. In this study, we developed LesionAttn, a fairness-aware algorithm that integrates clinical knowledge into model design by directing attention toward lesion regions, mirroring the diagnostic focus of clinicians. Combined with Pareto-frontier optimization for dual-objective model selection, LesionAttn balances fairness and predictive accuracy. Validated on two large-scale dermatological datasets, LesionAttn significantly mitigates gender bias while maintaining high diagnostic performance, outperforming existing bias mitigation algorithms. Our study highlights the potential of embedding clinical knowledge into AI development to advance both model performance and fairness, and further to foster interdisciplinary collaboration between clinicians and AI developers.
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