mlsanityData to 7 Oct 2026

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

arXiv 2211.13352 ↗First posted 23 Nov 2022eess.IV cs.CV cs.LGcited by 25

On the map: Diffusion models → Medical imaging and segmentation

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.

Key facts. Cited by 25 papers in the map (data through 7 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.

Builds on

9 of 15 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep

Other references · 8

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  3. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  4. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 184
  5. 2021Disparities in Dermatology AI: Assessments Using Diverse Clinical Imagescited by 132
  6. 2019A deep learning system for differential diagnosis of skin diseasescited by 118
  7. 2022Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithmcited by 35
  8. 2019DermGAN: Synthetic Generation of Clinical Skin Images with Pathologycited by 33

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 12; 2024: 8; 2025: 3; 2026: 2.

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  1. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  2. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  3. 2023Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Imagescited by 45
  4. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  5. 2023Augmenting medical image classifiers with synthetic data from latent diffusion modelscited by 21
  6. 2023Synthesising Rare Cataract Surgery Samples with Guided Diffusion Modelscited by 14
  7. 2024From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysiscited by 9
  8. 2025Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedbackcited by 9
  9. 2023Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detectioncited by 7
  10. 2023Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generationcited by 7
  11. 2024Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasetscited by 7
  12. 2025Are generative models fair? A study of racial bias in dermatological image generationcited by 7
13 more
  1. 2024S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Imagescited by 6
  2. 2024RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Featurescited by 5
  3. 2024PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africacited by 5
  4. 2023A Unified Framework for Generative Data Augmentation: A Comprehensive Surveycited by 4
  5. 2023Hate Speech Detection in Limited Data Contexts using Synthetic Data Generationcited by 3
  6. 2023Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Modelscited by 3
  7. 2024An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levelscited by 2
  8. 2023Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysiscited by 1
  9. 2024Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorizationcited by 1
  10. 2023Scaling ML Products At Startups: A Practitioner's Guidecited by 0
  11. 2025Prompt Mechanisms in Medical Imaging: A Comprehensive Surveycited by 0
  12. 2026Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classificationcited by 0
  13. 2026Limitations of Synthetic Data Generation in Specialized Data-Scarce Domainscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN
  2. 2025LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
  3. 2025Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis
  4. 2025Prompting Medical Vision-Language Models to Mitigate Diagnosis Bias by Generating Realistic Dermoscopic Images
  5. 2026DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation
  6. 2025DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis
  7. 2024FairSkin: Fair Diffusion for Skin Disease Image Generation
  8. 2025SkinDualGen: Prompt-Driven Diffusion for Simultaneous Image-Mask Generation in Skin Lesions