mlsanityData to 14 Oct 2026

Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis

arXiv 2510.16887 ↗First posted 19 Oct 2025cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Generative models, especially Diffusion Models, have demonstrated remarkable capability in generating high-quality synthetic data, including medical images. However, traditional class-conditioned generative models often struggle to generate images that accurately represent specific medical categories, limiting their usefulness for applications such as skin cancer diagnosis. To address this problem, we propose a classification-induced diffusion model, namely, Class-N-Diff, to simultaneously generate and classify dermoscopic images. Our Class-N-Diff model integrates a classifier within a diffusion model to guide image generation based on its class conditions. Thus, the model has better control over class-conditioned image synthesis, resulting in more realistic and diverse images. Additionally, the classifier demonstrates improved performance, highlighting its effectiveness for downstream diagnostic tasks. This unique integration in our Class-N-Diff makes it a robust tool for enhancing the quality and utility of diffusion model-based synthetic dermoscopic image generation. Our code is available at https://github.com/Munia03/Class-N-Diff.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 20 of its 38 references that are in the map. First posted on arXiv 19 Oct 2025.

Builds on

20 of 38 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis21 Mar

Other references · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2022Scalable Diffusion Models with Transformerscited by 6,054
  5. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
  6. 2019BCN20000: Dermoscopic Lesions in the Wildcited by 225
  7. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 185
  8. 2020A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Contextcited by 146
  9. 2021Disparities in Dermatology AI: Assessments Using Diverse Clinical Imagescited by 134
  10. 2023Generative models improve fairness of medical classifiers under distribution shiftscited by 90
  11. 2022FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosiscited by 59
  12. 2022Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classificationcited by 23
7 more
  1. 2023FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classificationcited by 19
  2. 2023Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosiscited by 14
  3. 2022The (de)biasing effect of GAN-based augmentation methods on skin lesion imagescited by 9
  4. 2024Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classificationcited by 5
  5. 2024FairSkin: Fair Diffusion for Skin Disease Image Generationcited by 4
  6. 2019Multi-Adversarial Variational Autoencoder Networkscited by 2
  7. 2025Prompting Medical Vision-Language Models to Mitigate Diagnosis Bias by Generating Realistic Dermoscopic Imagescited by 1

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  1. 2022Improving dermatology classifiers across populations using images generated by large diffusion models
  2. 2025Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
  3. 2024Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN
  4. 2025Diffusion models applied to skin and oral cancer classification
  5. 2026DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation
  6. 2023Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts
  7. 2023Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images
  8. 2025SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation in Melanoma Diagnosis