Abstract
Deep neural networks for chest X-ray classification achieve strong average performance, yet often underperform for specific demographic subgroups, raising critical concerns about clinical safety and equity. Existing debiasing methods frequently yield inconsistent improvements across datasets or attain fairness by degrading overall diagnostic utility, treating fairness as a post hoc constraint rather than a property of the learned representation. In this work, we propose Stride-Net (Sensitive Attribute Resilient Learning via Disentanglement and Learnable Masking with Embedding Alignment), a fairness-aware framework that learns disease-discriminative yet demographically invariant representations for chest X-ray analysis. Stride-Net operates at the patch level, using a learnable stride-based mask to select label-aligned image regions while suppressing sensitive attribute information through adversarial confusion loss. To anchor representations in clinical semantics and discourage shortcut learning, we further enforce semantic alignment between image features and BioBERT-based disease label embeddings via Group Optimal Transport. We evaluate Stride-Net on the MIMIC-CXR and CheXpert benchmarks across race and intersectional race-gender subgroups. Across architectures including ResNet and Vision Transformers, Stride-Net consistently improves fairness metrics while matching or exceeding baseline accuracy, achieving a more favorable accuracy-fairness trade-off than prior debiasing approaches. Our code is available at this https URL.
Abstract (translated)
深度神经网络在胸部X光分类中表现出强大的平均性能，但往往在特定的人口统计学亚群中的表现不佳，这引发了对临床安全性和公平性的重大担忧。现有的去偏方法通常会在数据集之间产生不一致的改进效果，或者通过降低整体诊断效用来实现公平性，这种做法将公平视为事后约束而非学习表示的一个属性。在这项工作中，我们提出了Stride-Net（敏感属性鲁棒学习通过解耦和可学习掩码与嵌入对齐），这是一种具有公平意识的框架，用于学习胸部X光分析中疾病识别但人口统计学不变的表示形式。 Stride-Net在补丁级别上运行，使用基于可学习步长的掩码选择标签对齐的图像区域，同时通过对抗混淆损失抑制敏感属性信息。为了将表示固定在临床语义上并避免捷径学习，我们进一步强制执行图像特征与BioBERT基础疾病标签嵌入之间的语义一致性，这是通过组最优传输实现的。 我们在MIMIC-CXR和CheXpert基准测试中评估了Stride-Net，在种族和交联种族性别亚群体之间。在包括ResNet和视觉变换器在内的各种架构中，Stride-Net始终提高了公平性指标，并且与基线准确度匹配或超过，实现了比先前的去偏方法更为有利的准确性-公平性权衡。 我们的代码可以在提供的链接处获取。
URL
https://arxiv.org/abs/2602.10875
