# FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification

arXiv: https://arxiv.org/abs/2412.16373 · HTML: https://mlsanity.com/paper/2412.16373/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 27 of 27)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [Learning Not to Learn: Training Deep Neural Networks with Biased Data](https://mlsanity.com/paper/1812.10352/) (2018)
- [Simple data balancing achieves competitive worst-group-accuracy](https://mlsanity.com/paper/2110.14503/) (2021)
- [Reading Race: AI Recognises Patient's Racial Identity In Medical Images](https://mlsanity.com/paper/2107.10356/) (2021)
- [CheXclusion: Fairness gaps in deep chest X-ray classifiers](https://mlsanity.com/paper/2003.00827/) (2020)
- [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019)
- [Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction](https://mlsanity.com/paper/1807.00199/) (2018)
- [Fairness by Learning Orthogonal Disentangled Representations](https://mlsanity.com/paper/2003.05707/) (2020)
- [Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation](https://mlsanity.com/paper/2106.12387/) (2021)
- [Improving the Fairness of Chest X-ray Classifiers](https://mlsanity.com/paper/2203.12609/) (2022)
- [FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models](https://mlsanity.com/paper/2407.00983/) (2024)
- [Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review](https://mlsanity.com/paper/2209.13177/) (2022)
- [Active Fairness in Algorithmic Decision Making](https://mlsanity.com/paper/1810.00031/) (2018)
- [Estimating and Improving Fairness with Adversarial Learning](https://mlsanity.com/paper/2103.04243/) (2021)
- [Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification](https://mlsanity.com/paper/2202.02832/) (2022)
- [FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning](https://mlsanity.com/paper/2208.10013/) (2022)
- [A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks](https://mlsanity.com/paper/2410.07593/) (2024)
- [On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations](https://mlsanity.com/paper/2301.01481/) (2023)
- [TARA: Training and Representation Alteration for AI Fairness and Domain Generalization](https://mlsanity.com/paper/2012.06387/) (2020)
- [A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models](https://mlsanity.com/paper/2207.04607/) (2022)
- [Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction](https://mlsanity.com/paper/2309.14392/) (2023)
- [Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?](https://mlsanity.com/paper/2309.12632/) (2023)

## Built on by (most cited first, 4 of 4)

- [FairEnc: A Fair Vision-Language Model with Fair Vision and Text Encoders for Glaucoma Detection](https://mlsanity.com/paper/2605.04882/) (2026)
- [Worst-Group Equalized Odds Regularization for Multi-Attribute Fair Medical Image Classification](https://mlsanity.com/paper/2605.19214/) (2026)
- [Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation](https://mlsanity.com/paper/2605.19230/) (2026)
- [Representation Disentanglement for Fair Chest X-Ray Diagnosis](https://mlsanity.com/paper/2610.06041/) (2026)

## Abstract (arXiv)

Recent advancements in deep learning have shown transformative potential in medical imaging, yet concerns about fairness persist due to performance disparities across demographic subgroups. Existing methods aim to address these biases by mitigating sensitive attributes in image data; however, these attributes often carry clinically relevant information, and their removal can compromise model performance-a highly undesirable outcome. To address this challenge, we propose Fair Re-fusion After Disentanglement (FairREAD), a novel, simple, and efficient framework that mitigates unfairness by re-integrating sensitive demographic attributes into fair image representations. FairREAD employs orthogonality constraints and adversarial training to disentangle demographic information while using a controlled re-fusion mechanism to preserve clinically relevant details. Additionally, subgroup-specific threshold adjustments ensure equitable performance across demographic groups. Comprehensive evaluations on a large-scale clinical X-ray dataset demonstrate that FairREAD significantly reduces unfairness metrics while maintaining diagnostic accuracy, establishing a new benchmark for fairness and performance in medical image classification.