# Improving dermatology classifiers across populations using images generated by large diffusion models

arXiv: https://arxiv.org/abs/2211.13352 · HTML: https://mlsanity.com/paper/2211.13352/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 9 of 9)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [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)
- [Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm](https://mlsanity.com/paper/2207.02942/) (2022)
- [DermGAN: Synthetic Generation of Clinical Skin Images with Pathology](https://mlsanity.com/paper/1911.08716/) (2019)

## Built on by (most cited first, 25 of 25)

- [Dream the Impossible: Outlier Imagination with Diffusion Models](https://mlsanity.com/paper/2309.13415/) (2023)
- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (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)
- [Data-Centric Foundation Models in Computational Healthcare: A Survey](https://mlsanity.com/paper/2401.02458/) (2024)
- [Augmenting medical image classifiers with synthetic data from latent diffusion models](https://mlsanity.com/paper/2308.12453/) (2023)
- [Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models](https://mlsanity.com/paper/2308.02587/) (2023)
- [From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysis](https://mlsanity.com/paper/2406.18375/) (2024)
- [Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback](https://mlsanity.com/paper/2506.12323/) (2025)
- [Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection](https://mlsanity.com/paper/2303.08216/) (2023)
- [Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation](https://mlsanity.com/paper/2312.03043/) (2023)
- [Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets](https://mlsanity.com/paper/2401.14497/) (2024)
- [Are generative models fair? A study of racial bias in dermatological image generation](https://mlsanity.com/paper/2501.11752/) (2025)
- [S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images](https://mlsanity.com/paper/2408.00191/) (2024)
- [RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Features](https://mlsanity.com/paper/2407.05683/) (2024)
- [PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa](https://mlsanity.com/paper/2411.04584/) (2024)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation](https://mlsanity.com/paper/2310.02876/) (2023)
- [Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models](https://mlsanity.com/paper/2311.11629/) (2023)
- [An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels](https://mlsanity.com/paper/2403.00452/) (2024)
- [Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis](https://mlsanity.com/paper/2309.04651/) (2023)
- [Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization](https://mlsanity.com/paper/2410.05114/) (2024)
- [Scaling ML Products At Startups: A Practitioner's Guide](https://mlsanity.com/paper/2304.10660/) (2023)
- [Prompt Mechanisms in Medical Imaging: A Comprehensive Survey](https://mlsanity.com/paper/2507.01055/) (2025)
- [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://mlsanity.com/paper/2607.12987/) (2026)
- [Limitations of Synthetic Data Generation in Specialized Data-Scarce Domains](https://mlsanity.com/paper/2608.13729/) (2026)

## Abstract (arXiv)

Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annotation offer the best means for improving representation, new computational approaches for generating training data may also aid in mitigating the effects of sampling bias. In this paper, we show that DALL·E 2, a large-scale text-to-image diffusion model, can produce photorealistic images of skin disease across skin types. Using the Fitzpatrick 17k dataset as a benchmark, we demonstrate that augmenting training data with DALL·E 2-generated synthetic images improves classification of skin disease overall and especially for underrepresented groups.