arXiv cs.AI· Giovanni dos Santos Franco, Felipe Mahlow, Ellison Fernando Cardoso, Jos\'e Adenaldo Santos Bittencourt, Felipe Fanchini·· 14 小时前AI 评分18
量子机器学习医疗影像公平性审计：DDI 与 CheXpert 上的敏感亚组评估
Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive Subgroups
AI 导读
一项实证公平性审计对比了经典与量子分类器在医疗影像上的亚组表现：在 DDI 皮肤病变数据（敏感属性为 Fitzpatrick 皮肤类型）和 CheXpert 衍生胸片数据（敏感属性为年龄组）上。
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