mlsanityData to 7 Oct 2026

Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models

arXiv 2306.09004 ↗First posted 15 Jun 2023eess.IV cs.CVcited by 10

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. To address this challenge, we propose a novel method for multi-expert prediction using diffusion models. Our method leverages the diffusion-based approach to incorporate information from multiple annotations and fuse it into a unified segmentation map that reflects the consensus of multiple experts. We evaluate the performance of our method on several datasets of medical segmentation annotated by multiple experts and compare it with state-of-the-art methods. Our results demonstrate the effectiveness and robustness of the proposed method. Our code is publicly available at https://github.com/tomeramit/Annotator-Consensus-Prediction.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 21 of its 28 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

21 of 28 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Ambiguous Medical Image Segmentation using Diffusion Models10 Apr
  2. 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec
  3. 2021Label-Efficient Semantic Segmentation with Diffusion Models6 Dec
  4. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Models1 Dec

Other references · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  7. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  8. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  9. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  10. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  11. 2021Variational Diffusion Modelscited by 1,145
  12. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
5 more
  1. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  2. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  3. 2021A Variational Perspective on Diffusion-Based Generative Models and Score Matchingcited by 182
  4. 2021Noise Estimation for Generative Diffusion Modelscited by 94
  5. 2017Who Said What: Modeling Individual Labelers Improves Classificationcited by 77

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 4; 2026: 1.

10 papers.

  1. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  2. 2025Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Freecited by 9
  3. 2024Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoningcited by 6
  4. 2025Applications of Large Models in Medicinecited by 6
  5. 2025Conditional diffusion model with spatial attention and latent embedding for medical image segmentationcited by 5
  6. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  7. 2023ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learningcited by 0
  8. 2024Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identificationcited by 0
  9. 2025Explaining Uncertainty in Multiple Sclerosis Cortical Lesion Segmentation Beyond Prediction Errorscited by 0
  10. 2026Rethinking Diffusion Segmentation: When Does It Rely on Its Noisy State, and Does Diffusion Matter?cited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
  2. 2024Analysing Diffusion Segmentation for Medical Images
  3. 2025Diffusion Based Ambiguous Image Segmentation
  4. 2023Robust semi-supervised segmentation with timestep ensembling diffusion models
  5. 2024Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models
  6. 2024Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracy
  7. 2024Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models
  8. 2026SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentation