# From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysis

arXiv: https://arxiv.org/abs/2406.18375 · HTML: https://mlsanity.com/paper/2406.18375/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Visual Transformers: Token-based Image Representation and Processing for Computer Vision](https://mlsanity.com/paper/2006.03677/) (2020)
- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [Disparities in Dermatology AI: Assessments Using Diverse Clinical Images](https://mlsanity.com/paper/2111.08006/) (2021)
- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [Generative models improve fairness of medical classifiers under distribution shifts](https://mlsanity.com/paper/2304.09218/) (2023)
- [Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images](https://mlsanity.com/paper/2301.04802/) (2023)
- [DermGAN: Synthetic Generation of Clinical Skin Images with Pathology](https://mlsanity.com/paper/1911.08716/) (2019)
- [Improving dermatology classifiers across populations using images generated by large diffusion models](https://mlsanity.com/paper/2211.13352/) (2022)
- [Augmenting medical image classifiers with synthetic data from latent diffusion models](https://mlsanity.com/paper/2308.12453/) (2023)
- [Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation](https://mlsanity.com/paper/2307.03157/) (2023)

## Built on by (most cited first, 9 of 9)

- [Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback](https://mlsanity.com/paper/2506.12323/) (2025)
- [On Fairness of Unified Multimodal Large Language Model for Image Generation](https://mlsanity.com/paper/2502.03429/) (2025)
- [MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis](https://mlsanity.com/paper/2507.00377/) (2025)
- [The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generation](https://mlsanity.com/paper/2502.07516/) (2025)
- [eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases](https://mlsanity.com/paper/2508.18608/) (2025)
- [CRAFT: Clinical Reward-Aligned Finetuning for Medical Image Synthesis](https://mlsanity.com/paper/2605.12650/) (2026)
- [Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis](https://mlsanity.com/paper/2507.17860/) (2025)
- [Synthetic Melanoma Image Generation and Evaluation Using Generative Adversarial Networks](https://mlsanity.com/paper/2603.13497/) (2026)
- [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://mlsanity.com/paper/2607.12987/) (2026)

## Abstract (arXiv)

AI-based diagnoses have demonstrated dermatologist-level performance in classifying skin cancer. However, such systems are prone to under-performing when tested on data from minority groups that lack sufficient representation in the training sets. Although data collection and annotation offer the best means for promoting minority groups, these processes are costly and time-consuming. Prior works have suggested that data from majority groups may serve as a valuable information source to supplement the training of diagnosis tools for minority groups. In this work, we propose an effective diffusion-based augmentation framework that maximizes the use of rich information from majority groups to benefit minority groups. Using groups with different skin types as a case study, our results show that the proposed framework can generate synthetic images that improve diagnostic results for the minority groups, even when there is little or no reference data from these target groups. The practical value of our work is evident in medical imaging analysis, where under-diagnosis persists as a problem for certain groups due to insufficient representation.