对比逻辑回归与CNN在皮肤癌检测中的性别偏差，发现模型对性别分布不敏感但男患者表现更好。
Robustness and sex differences in skin cancer detection: logistic regression vs CNNs
- 用手工特征和预训练ResNet-50建模，评估不同性别构成数据下的模型鲁棒性。
- CNN对男性患者准确率和AUROC显著高于女性，但整体对性别分布保持稳健。
- 研究揭示了深度学习在医疗诊断中潜在的性别偏差，适合关注模型公平性的研究者。
深度学习在皮肤癌检测中表现优异，但结果可复现性与偏差问题仍存。本研究复现了此前阿尔茨海默病检测研究的方法（使用不同数据集，相同分析流程），探讨逻辑回归（LR）与卷积神经网络（CNN）在患者性别上的鲁棒性。基于PAD-UFES-20数据集，采用反映皮肤科指南（ABCDE和7点检查表）的手工特征训练LR，并使用预训练的ResNet-50模型。通过多种具有不同性别比例的训练数据集评估模型性能。结果显示，LR与CNN均对性别分布变化具有鲁棒性，但CNN在男性患者上的准确率（ACC）和受试者工作特征曲线下面积（AUROC）显著高于女性患者。相关数据与代码已公开（https://github.com/nikodice4/Skin-cancer-detection-sex-bias）。
原文摘要 · Abstract (English)
Deep learning has been reported to achieve high performances in the detection of skin cancer, yet many challenges regarding the reproducibility of results and biases remain. This study is a replication (different data, same analysis) of a previous study on Alzheimer's disease detection, which studied the robustness of logistic regression (LR) and convolutional neural networks (CNN) across patient sexes. We explore sex bias in skin cancer detection, using the PAD-UFES-20 dataset with LR trained on handcrafted features reflecting dermatological guidelines (ABCDE and the 7-point checklist), and a pre-trained ResNet-50 model. We evaluate these models in alignment with the replicated study: across multiple training datasets with varied sex composition to determine their robustness. Our results show that both the LR and the CNN were robust to the sex distribution, but the results also revealed that the CNN had a significantly higher accuracy (ACC) and area under the receiver operating characteristics (AUROC) for male patients compared to female patients. The data and relevant scripts to reproduce our results are publicly available (https://github.com/ nikodice4/Skin-cancer-detection-sex-bias).
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