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 https://github.com/Daraksh/Fairness_StrideNet.

翻译：用于胸部X光分类的深度神经网络虽能实现较强的平均性能，但在特定人口统计亚组中往往表现不佳，这引发了关于临床安全性与公平性的重大关切。现有的去偏方法常在不同数据集上产生不一致的改进效果，或通过降低整体诊断效用来实现公平性，将公平性视为事后约束而非学习表征的内在属性。本研究提出Stride-Net（通过解耦、可学习掩码与嵌入对齐的敏感属性弹性学习框架），这是一个公平感知的框架，旨在为胸部X光分析学习具有疾病判别力且对人口统计信息保持不变的表示。Stride-Net在图像块级别进行操作，利用可学习的基于步长的掩码来选择与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。为使表征锚定于临床语义并避免捷径学习，我们进一步通过群组最优传输强制实现图像特征与基于BioBERT的疾病标签嵌入之间的语义对齐。我们在MIMIC-CXR和CheXpert基准数据集上，针对种族及跨种族-性别的交叉亚组评估Stride-Net。在包括ResNet和Vision Transformer在内的多种架构中，Stride-Net在保持或超越基线准确率的同时，持续提升了公平性指标，相较于先前的去偏方法实现了更优的准确率-公平性权衡。代码已开源：https://github.com/Daraksh/Fairness_StrideNet。