mlsanityData to 7 Oct 2026

DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models

arXiv 2304.13416 ↗First posted 26 Apr 2023eess.IV cs.CVcited by 6

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Dataset expansion can effectively alleviate the problem of data scarcity for medical image segmentation, due to privacy concerns and labeling difficulties. However, existing expansion algorithms still face great challenges due to their inability of guaranteeing the diversity of synthesized images with paired segmentation masks. In recent years, Diffusion Probabilistic Models (DPMs) have shown powerful image synthesis performance, even better than Generative Adversarial Networks. Based on this insight, we propose an approach called DiffuseExpand for expanding datasets for 2D medical image segmentation using DPM, which first samples a variety of masks from Gaussian noise to ensure the diversity, and then synthesizes images to ensure the alignment of images and masks. After that, DiffuseExpand chooses high-quality samples to further enhance the effectiveness of data expansion. Our comparison and ablation experiments on COVID-19 and CGMH Pelvis datasets demonstrate the effectiveness of DiffuseExpand. Our code is released at https://github.com/shaoshitong/DiffuseExpand.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 20 of its 42 references that are in the map. First posted on arXiv 26 Apr 2023.

Builds on

20 of 42 references.

References · 20

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  8. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  9. 2016Improved Techniques for Training GANscited by 2,938
  10. 2014Conditional Generative Adversarial Netscited by 2,152
  11. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  12. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
8 more
  1. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  2. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 924
  3. 2020Medical Image Segmentation Using Deep Learning: A Surveycited by 163
  4. 2022Privacy for Free: How does Dataset Condensation Help Privacy?cited by 119
  5. 2020COVID-19 Image Data Collectioncited by 100
  6. 2017Synthetic Medical Images from Dual Generative Adversarial Networkscited by 32
  7. 2019XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generationcited by 28
  8. 2018Learning Implicit Brain MRI Manifolds with Deep Learningcited by 20

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 2.

6 papers.

  1. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  2. 2025Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentationcited by 21
  3. 2023Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelscited by 19
  4. 2024PairAug: What Can Augmented Image-Text Pairs Do for Radiology?cited by 10
  5. 2024Bag of Design Choices for Inference of High-Resolution Masked Generative Transformercited by 7
  6. 2025SimCroP: Radiograph Representation Learning with Similarity-driven Cross-granularity Pre-trainingcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
  2. 2023DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
  3. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
  4. 2024Analysing Diffusion Segmentation for Medical Images
  5. 2023Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation
  6. 2024Comparative Analysis of Diffusion Generative Models in Computational Pathology
  7. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
  8. 2023Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation