# FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning

arXiv: https://arxiv.org/abs/2208.10013 · HTML: https://mlsanity.com/paper/2208.10013/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation](https://mlsanity.com/paper/1911.11834/) (2019)
- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [Adversarial Removal of Demographic Attributes from Text Data](https://mlsanity.com/paper/1808.06640/) (2018)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification](https://mlsanity.com/paper/2103.14267/) (2021)
- [Fairness in Deep Learning: A Computational Perspective](https://mlsanity.com/paper/1908.08843/) (2019)
- [Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings](https://mlsanity.com/paper/1809.02169/) (2018)
- [Investigating Bias and Fairness in Facial Expression Recognition](https://mlsanity.com/paper/2007.10075/) (2020)
- [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)
- [Post-processing for Individual Fairness](https://mlsanity.com/paper/2110.13796/) (2021)
- [FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis](https://mlsanity.com/paper/2203.02110/) (2022)
- [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)
- [Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models](https://mlsanity.com/paper/2203.01584/) (2022)
- [Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data](https://mlsanity.com/paper/1910.03910/) (2019)
- [Contrastive Domain Adaptation](https://mlsanity.com/paper/2103.15566/) (2021)
- [Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm](https://mlsanity.com/paper/2207.02942/) (2022)
- [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)
- [Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting](https://mlsanity.com/paper/1905.02793/) (2019)

## Built on by (most cited first, 22 of 22)

- [Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review](https://mlsanity.com/paper/2209.13177/) (2022)
- [Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey](https://mlsanity.com/paper/2211.05617/) (2022)
- [BiasPruner: Debiased Continual Learning for Medical Image Classification](https://mlsanity.com/paper/2407.08609/) (2024)
- [STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification](https://mlsanity.com/paper/2503.06277/) (2025)
- [Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets](https://mlsanity.com/paper/2401.14497/) (2024)
- [PatchAlign:Fair and Accurate Skin Disease Image Classification by Alignment with Clinical Labels](https://mlsanity.com/paper/2409.04975/) (2024)
- [FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification](https://mlsanity.com/paper/2307.11654/) (2023)
- [FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification](https://mlsanity.com/paper/2412.16373/) (2024)
- [Bias and Generalizability of Foundation Models across Datasets in Breast Mammography](https://mlsanity.com/paper/2505.10579/) (2025)
- [AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets](https://mlsanity.com/paper/2307.13897/) (2023)
- [Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning](https://mlsanity.com/paper/2509.00745/) (2025)
- [Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learning](https://mlsanity.com/paper/2401.15111/) (2024)
- [Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression](https://mlsanity.com/paper/2506.11627/) (2025)
- [Debiasify: Self-Distillation for Unsupervised Bias Mitigation](https://mlsanity.com/paper/2411.00711/) (2024)
- [LesionTABE: Equitable AI for Skin Lesion Detection](https://mlsanity.com/paper/2601.03090/) (2026)
- [Multimodal system for skin cancer detection](https://mlsanity.com/paper/2601.14822/) (2026)
- [Exploring the Impact of Skin Color on Skin Lesion Segmentation](https://mlsanity.com/paper/2603.29694/) (2026)
- [Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance](https://mlsanity.com/paper/2604.10904/) (2026)
- [Worst-Group Equalized Odds Regularization for Multi-Attribute Fair Medical Image Classification](https://mlsanity.com/paper/2605.19214/) (2026)
- [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://mlsanity.com/paper/2607.12987/) (2026)
- [Bias Mitigation in Face Recognition via Demographic-based Supervised Contrastive Learning](https://mlsanity.com/paper/2608.12971/) (2026)
- [MIFR: A Modality-Invariant and Fair Representation Framework for Skin Disease Classification](https://mlsanity.com/paper/2608.18774/) (2026)

## Abstract (arXiv)

Deep learning models have achieved great success in automating skin lesion diagnosis. However, the ethnic disparity in these models' predictions, where lesions on darker skin types are usually underrepresented and have lower diagnosis accuracy, receives little attention. In this paper, we propose FairDisCo, a disentanglement deep learning framework with contrastive learning that utilizes an additional network branch to remove sensitive attributes, i.e. skin-type information from representations for fairness and another contrastive branch to enhance feature extraction. We compare FairDisCo to three fairness methods, namely, resampling, reweighting, and attribute-aware, on two newly released skin lesion datasets with different skin types: Fitzpatrick17k and Diverse Dermatology Images (DDI). We adapt two fairness-based metrics DPM and EOM for our multiple classes and sensitive attributes task, highlighting the skin-type bias in skin lesion classification. Extensive experimental evaluation demonstrates the effectiveness of FairDisCo, with fairer and superior performance on skin lesion classification tasks.