mlsanityData to 14 Oct 2026

Diffusion models applied to skin and oral cancer classification

arXiv 2504.00026 ↗First posted 28 Mar 2025eess.IV cs.AI cs.CVcited by 3

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral cancer, the diffusion model demonstrated competitive performance compared to state-of-the-art deep learning models like Convolutional Neural Networks (CNNs) and Transformers. Specifically, for the PAD-UFES-20 dataset, the model achieved a balanced accuracy of 0.6457 for six-class classification and 0.8357 for binary classification (cancer vs. non-cancer). For the P-NDB-UFES dataset, it attained a balanced accuracy of 0.9050. These results suggest that diffusion models are viable models for classifying medical images of skin and oral lesions. In addition, we investigate the robustness of the model trained on PAD-UFES-20 for skin cancer but tested on the clinical images of the HIBA dataset.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 22 of its 36 references that are in the map. First posted on arXiv 28 Mar 2025.

Builds on

22 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification19 Mar
  2. 2022Diffusion Probabilistic Models beat GANs on Medical Images14 Dec

Other references · 20

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  8. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  9. 2021Rethinking Spatial Dimensions of Vision Transformerscited by 361
  10. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 327
  11. 2022Is synthetic data from generative models ready for image recognition?cited by 320
  12. 2023Effective Data Augmentation With Diffusion Modelscited by 283
8 more
  1. 2021Co-Scale Conv-Attentional Image Transformerscited by 224
  2. 2021Are Transformers More Robust Than CNNs?cited by 187
  3. 2020PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphonescited by 118
  4. 2022CARD: Classification and Regression Diffusion Modelscited by 103
  5. 2023Diffusion Models Beat GANs on Image Classificationcited by 58
  6. 2019The impact of patient clinical information on automated skin cancer detectioncited by 21
  7. 2021A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Informationcited by 5
  8. 2022Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasetscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regressioncited by 1
  2. 2026Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classificationcited by 0
  3. 2026Generative AI-Based Data Augmentation for Oral Lesion Classification: The PhotoMOCI Dataset and Benchmarkcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
  2. 2025Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis
  3. 2023Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images
  4. 2024Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN
  5. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey
  6. 2026DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation
  7. 2022Improving dermatology classifiers across populations using images generated by large diffusion models
  8. 2026CNNs, Transformers, Hybrid, and Vision Language Models for Skin Cancer Detection