mlsanityData to 7 Oct 2026

Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images

arXiv 2410.09210 ↗First posted 11 Oct 2024cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Manual annotation of 3D medical images for segmentation tasks is tedious and time-consuming. Moreover, data privacy limits the applicability of crowd sourcing to perform data annotation in medical domains. As a result, training deep neural networks for medical image segmentation can be challenging. We introduce a new source-free Unsupervised Domain Adaptation (UDA) method to address this problem. Our idea is based on estimating the internally learned distribution of a relevant source domain by a base model and then generating pseudo-labels that are used for enhancing the model refinement through self-training. We demonstrate that our approach leads to SOTA performance on a real-world 3D medical dataset.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 41 of its 72 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

41 of 72 references.

References · 41

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2023Segment Anythingcited by 8,158
  4. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  6. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  7. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,079
  8. 2020Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptationcited by 844
  9. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  10. 2018Learning to Adapt Structured Output Space for Semantic Segmentationcited by 584
  11. 2014Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFscited by 552
  12. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
29 more
  1. 2023Segment Anything in High Qualitycited by 373
  2. 2018A Survey of Unsupervised Deep Domain Adaptationcited by 361
  3. 2019Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptationcited by 235
  4. 2020Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentationcited by 134
  5. 2016Multivariate mixture model for myocardium segmentation combining multi-source imagescited by 133
  6. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 79
  7. 2019AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Datacited by 77
  8. 2018Fully Convolutional Adaptation Networks for Semantic Segmentationcited by 75
  9. 2018SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truthcited by 68
  10. 2023Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptationcited by 63
  11. 2022SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGcited by 53
  12. 2019Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replaycited by 51
  13. 2020Unsupervised Model Adaptation for Continual Semantic Segmentationcited by 42
  14. 2023BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizabilitycited by 42
  15. 2021Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentationcited by 39
  16. 2018Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGANcited by 31
  17. 2021Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptationcited by 24
  18. 2021Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentationcited by 23
  19. 2021Cognitively Inspired Learning of Incremental Drifting Conceptscited by 16
  20. 2022Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentationcited by 16
  21. 2022Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptationcited by 14
  22. 2023Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioningcited by 14
  23. 2022Unsupervised Model Adaptation for Source-free Segmentation of Medical Imagescited by 13
  24. 2019Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignmentcited by 12
  25. 2021Domain Adaptation for the Segmentation of Confidential Medical Imagescited by 12
  26. 2019Zero-Shot Image Classification Using Coupled Dictionary Embeddingcited by 10
  27. 2017Joint Dictionaries for Zero-Shot Learningcited by 9
  28. 2023Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancementcited by 8
  29. 2023A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentationcited by 5

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025RSTR: Reducing SpatioTemporal Redundancy in Diffusion Transformerscited by 1