arXiv cs.AI· Giovanni dos Santos Franco, Felipe Mahlow, Ellison Fernando Cardoso, Jos\'e Adenaldo Santos Bittencourt, Felipe Fanchini·· 2 小时前AI 评分36
量子机器学习医学影像公平性审计：跨敏感亚组研究
Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive Subgroups
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一项研究对经典与量子分类器在医学影像任务上做了亚组公平性审计，假设量子机器学习（QML）并非天然公平。
正文 · 原文
Abstract:Quantum machine learning (QML) is increasingly explored for medical classification, yet its behaviour under subgroup-aware fairness criteria remains understudied. We present an empirical fairness audit of classical and quantum classifiers for medical imaging, assuming QML is not inherently fair. Two scenarios are evaluated: dermatological lesions on DDI (sensitive attribute: Fitzpatrick skin type) and chest X-rays on CheXpert-derived data (sensitive attribute: age group). Using ResNet-18 embeddings, we train SVM, MLP, QSVM and QCNN under a baseline and three mitigation settings: differentiable Equalized Odds (EO) regularisation, Gradient Reversal Learning (GRL), and both combined. Foundation models (PanDerm, EVA-X) serve as external references. Quantum classifiers may achieve competitive aggregate performance while still exhibiting subgroup disparities. Since a smaller subgroup AUC gap may stem from degrading the best group rather than improving the worst, each mitigation is also tested against a pre-specified non-inferiority margin relative to its baseline. Under this criterion, several configurations with the smallest disparity, notably QCNN with GRL on DDI, are not clinically acceptable due to loss of global discrimination. On DDI, QSVM with GRL+EO performed best and approached PanDerm in the performance/fairness trade-off; on CheXpert, QCNN with GRL achieved the highest aggregate performance but increased disparity, and EVA-X remained stronger. Fairness effects are model- and dataset-dependent, so QML requires empirical subgroup-aware evaluation. All quantum models run in classical simulation; we claim no quantum advantage.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.37819 [cs.LG] |
| (or arXiv:2610.37819v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37819 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Felipe Mahlow [view email]
[v1]
Tue, 27 Oct 2026 18:33:17 UTC (2,630 KB)
来源：arXiv cs.AI · arxiv.org