Robustness and sex differences in skin cancer detection: logistic regression vs CNNs

• URL: http://arxiv.org/abs/2504.11415v1
• Date: Tue, 15 Apr 2025 17:31:46 GMT
• Title: Robustness and sex differences in skin cancer detection: logistic regression vs CNNs
• Authors: Nikolette Pedersen, Regitze Sydendal, Andreas Wulff, Ralf Raumanns, Eike Petersen, Veronika Cheplygina,
• Abstract summary: This study is a replication of a study on Alzheimer's disease which studied robustness of logistic regression (LR) and convolutional neural networks (CNN) across patient sexes.<n>We evaluate these models in alignment with [28]: across multiple training datasets with varied sex composition to determine their robustness.<n>Our results show that both the LR and the CNN were robust to the sex distributions, 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 than for female patients.
• Score: 1.758593528245578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 study on Alzheimer's disease [28] which studied 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 [28]: 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 distributions, 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 than for female patients. We hope these findings to contribute to the growing field of investigating potential bias in popular medical machine learning methods. The data and relevant scripts to reproduce our results can be found in our Github.

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