Representation Disentanglement for Fair Chest X-Ray Diagnosis

Yujie Sun; Ruizhe Li; Xiaowu Sun

cs.CVOct 5, 2026

Representation Disentanglement for Fair Chest X-Ray Diagnosis

Authors: Yujie Sun, Ruizhe Li, Xiaowu Sun

Organizations: Academy of Artificial Intelligence and Advanced Technology, Xi’an Jiaotong-Liverpool University, China · Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, UK

Abstract

Deep learning has advanced chest X-ray (CXR) diagnosis, yet demographic biases in learned representations may contribute to performance disparities across intersectional groups. We propose a single-encoder framework combining dual-level decorrelation with prototype-guided cross-group contrastive learning to reduce demographic dependence while accounting for within-class variation. We further propose Demographic Representation Alignment Reduction (DRAR), a new metric that quantifies the reduction in demographic structure within disease representations. The framework is evaluated on four classification tasks using 34,809 CheXpert test images across eight intersectional groups, defined by age, sex and ethnicity. Compared with empirical risk minimization (ERM), our method reduces the mean equalized-odds gap from 15.41% to 10.86% and the AUC gap from 5.95% to 5.01%. Our method achieves a DRAR of 59.04% relative to ERM, with only a slight decrease in mean AUC. These results demonstrate that representation disentanglement can reduce demographic bias and improve intersectional fairness. Code is available at https://github.com/06Yujie/Fair-Medical-Imaging.

Figures & tables

Figure 1: Overview of the proposed framework. A shared encoder learns disease and attribute spaces through prototype-guided cross-group alignment and dual-level decorrelation.


Method	BACC $\uparrow$	AUC $\uparrow$	F1 $\uparrow$	$\Delta_{\rm EO}\downarrow$	$\Delta_{\rm AUC}\downarrow$	$F_{\rm EO}\uparrow$	$F_{\rm AUC}\uparrow$
ERM	76.44	83.80	66.22	15.41	5.95	0.00	0.00
GroupDRO	74.99	82.07	65.22	12.43	5.42	18.85	10.01
ReSample	75.98	83.09	65.14	14.30	5.78	5.21	0.39
DANN	74.11	81.83	66.11	15.73	5.56	-1.60	3.50
FairAdaBN	74.70	82.02	65.13	14.10	5.95	1.33	-1.67

Table 1: Four-task mean diagnostic and intersectional fairness results (%).

F

denotes the corresponding FATE score.


	Demographic Prediction Accuracy $\downarrow$
Method	Age	Sex	Ethnicity	DRAR $\uparrow$
ERM	72.32 $\pm$ 1.66	86.22 $\pm$ 2.12	62.00 $\pm$ 1.34	0.00
GroupDRO	71.98 $\pm$ 1.73	85.63 $\pm$ 2.01	61.51 $\pm$ 1.48	22.22
ReSample	72.20 $\pm$ 1.90	85.80 $\pm$ 2.44	61.99 $\pm$ 1.40	2.81
DANN	70.94 $\pm$ 2.27	85.95 $\pm$ 1.83	61.94 $\pm$ 1.13	20.92

Table 2: Representation-level fairness results (%). Demographic prediction accuracy is reported as mean

\pm

standard deviation across four tasks. “–” denotes methods using demographic attributes at inference.

Figure 2: Pleural Effusion feature analysis. (a,b) t-SNE plots for ERM and our method. (c,d) Mean Euclidean distances between group–label representations in original feature space.

Method	$\mathcal{L}_{\mathrm{disease}}$ $+\mathcal{L}_{\mathrm{attribute}}$	$\mathcal{L}_{\mathrm{decorr}}$ $+\mathcal{L}_{\mathrm{dCor}}$	$\mathcal{L}_{d\text{-}con}$ $+\mathcal{L}_{\mathrm{proto}}$	BACC $\uparrow$	AUC $\uparrow$	F1 $\uparrow$	$\Delta_{\mathrm{EO}}\downarrow$	$\Delta_{\mathrm{AUC}}\downarrow$
Ablation 1	✓			73.59	82.21	62.60	16.67	5.30
Ablation 2	✓	✓		75.43	83.10	64.49	14.28	5.21
Ablation 3	✓		✓	75.15	82.36	64.09	17.72	5.79
Full model	✓	✓	✓	75.47	83.36	65.34	10.86	5.01

Table 3: Ablation study of the proposed components.

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Medical Image Generation Diffusion Alignment Diffusion Models Diagnosis Synthesis

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Medical imaging is central to modern diagnostics, and artificial intelligence (AI) systems are increasingly used to support image-based analysis by improving efficiency, accuracy, and access to care. However, inequities in healthcare access and differential disease prevalence create severe demographic imbalances in clinical image data. Such imbalances are compounded by the fact that diseases can manifest with distinct features across demographic groups, rendering certain phenotypic presentations naturally rare. AI models trained on such imbalanced data risk perpetuating diagnostic bias and widening healthcare disparities. Here we introduce FairGen, a fairness-aware diffusion framework that synthesizes demographically balanced medical images while preserving pathology-relevant visual features. By embedding physician-aligned preferences into the generation process, FairGen improves subgroup coverage during synthesis and downstream classification. Applied to dermatology, radiology, and neuroimaging benchmark tasks, FairGen achieves fairness improvements of 95.9% for skin images, 80.0% for chest radiography, and 35.2% for brain MRI, while maintaining competitive diagnostic accuracy relative to models trained on original clinical data. Clinician-facing expert review and external validation on independent cohorts further support that these gains extend beyond standard fidelity metrics and are not confined to the original in-distribution datasets.

Abstract

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