Stride-Net: Fairness-Aware Disentangled Representation Learning for Chest X-Ray Diagnosis

TL;DR

Stride-Net is a novel framework for chest X-ray diagnosis that learns disease-related features invariant to demographic attributes, improving fairness without sacrificing diagnostic accuracy by using patch-level masking and semantic alignment techniques.

Contribution

It introduces a fairness-aware disentangled learning method with a learnable patch mask and semantic alignment, addressing bias in medical imaging models.

Findings

Consistently improves fairness metrics across datasets and architectures.

Maintains or exceeds baseline diagnostic accuracy.

Achieves better fairness-accuracy trade-offs than previous methods.

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