mlsanityData to 7 Oct 2026

Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation

arXiv 2303.12313 ↗First posted 22 Mar 2023cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

The Diffusion Probabilistic Model (DPM) has emerged as a highly effective generative model in the field of computer vision. Its intermediate latent vectors offer rich semantic information, making it an attractive option for various downstream tasks such as segmentation and detection. In order to explore its potential further, we have taken a step forward and considered a more complex scenario in the medical image domain, specifically, under an unsupervised adaptation condition. To this end, we propose a Diffusion-based and Prototype-guided network (DP-Net) for unsupervised domain adaptive segmentation. Concretely, our DP-Net consists of two stages: 1) Distribution Aligned Diffusion (DADiff), which involves training a domain discriminator to minimize the difference between the intermediate features generated by the DPM, thereby aligning the inter-domain distribution; and 2) Prototype-guided Consistency Learning (PCL), which utilizes feature centroids as prototypes and applies a prototype-guided loss to ensure that the segmentor learns consistent content from both source and target domains. Our approach is evaluated on fundus datasets through a series of experiments, which demonstrate that the performance of the proposed method is reliable and outperforms state-of-the-art methods. Our work presents a promising direction for using DPM in complex medical image scenarios, opening up new possibilities for further research in medical imaging.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 25 of its 29 references that are in the map. First posted on arXiv 22 Mar 2023.

Builds on

25 of 29 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Label-Efficient Semantic Segmentation with Diffusion Models6 Dec
  2. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Models1 Dec

Other references · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  5. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  6. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  7. 2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
  8. 2016FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptationcited by 242
  9. 2019REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographscited by 226
  10. 2020Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentationcited by 222
  11. 2020Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detectorcited by 106
  12. 2021Repurposing GANs for One-shot Semantic Part Segmentationcited by 88
11 more
  1. 2020Generative Hierarchical Features from Synthesizing Imagescited by 83
  2. 2021TVT: Transferable Vision Transformer for Unsupervised Domain Adaptationcited by 81
  3. 2018StainGAN: Stain Style Transfer for Digital Histological Imagescited by 68
  4. 2021Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labelingcited by 61
  5. 2022Safe Self-Refinement for Transformer-based Domain Adaptationcited by 61
  6. 2020Source-Relaxed Domain Adaptation for Image Segmentationcited by 52
  7. 2018Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentationcited by 49
  8. 2019Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentationcited by 46
  9. 2019Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentationcited by 44
  10. 2022Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learningcited by 17
  11. 2022Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularizationcited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation
  2. 2024Diffusion Features to Bridge Domain Gap for Semantic Segmentation
  3. 2023Robust semi-supervised segmentation with timestep ensembling diffusion models
  4. 2024Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models
  5. 2024Generalizable Single-Source Cross-modality Medical Image Segmentation via Invariant Causal Mechanisms
  6. 2024Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracy
  7. 2023Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation
  8. 2024AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation