mlsanityData to 7 Oct 2026

RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation

arXiv 2410.01110 ↗First posted 1 Oct 2024cs.CVcited by 6

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Abstract (arXiv)

Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current FSMIS models are all trained and deployed on the same data domain, as is not consistent with the clinical reality that medical imaging data is always across different data domains (e.g. imaging modalities, institutions and equipment sequences). How to enhance the FSMIS models to generalize well across the different specific medical imaging domains? In this paper, we focus on the matching mechanism of the few-shot semantic segmentation models and introduce an Earth Mover's Distance (EMD) calculation based domain robust matching mechanism for the cross-domain scenario. Specifically, we formulate the EMD transportation process between the foreground support-query features, the texture structure aware weights generation method, which proposes to perform the sobel based image gradient calculation over the nodes, is introduced in the EMD matching flow to restrain the domain relevant nodes. Besides, the point set level distance measurement metric is introduced to calculated the cost for the transportation from support set nodes to query set nodes. To evaluate the performance of our model, we conduct experiments on three scenarios (i.e., cross-modal, cross-sequence and cross-institution), which includes eight medical datasets and involves three body regions, and the results demonstrate that our model achieves the SoTA performance against the compared models.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 40 of its 73 references that are in the map. First posted on arXiv 1 Oct 2024.

Builds on

40 of 73 references.

References · 40

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  3. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 307
  4. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  5. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  6. 2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
  7. 2016Multivariate mixture model for myocardium segmentation combining multi-source imagescited by 133
  8. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  9. 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
  10. 2016Unsupervised domain adaptation in brain lesion segmentation with adversarial networkscited by 125
  11. 2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
  12. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 118
28 more
  1. 2022Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentationcited by 114
  2. 2022Self-Support Few-Shot Semantic Segmentationcited by 96
  3. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
  4. 2019'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Imagescited by 69
  5. 2021Mining Latent Classes for Few-shot Segmentationcited by 69
  6. 2022Feature-Proxy Transformer for Few-Shot Segmentationcited by 58
  7. 2022Learning Non-target Knowledge for Few-shot Semantic Segmentationcited by 57
  8. 2023Domain Generalization for Medical Image Analysis: A Reviewcited by 47
  9. 2021Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalizationcited by 45
  10. 2022Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentationcited by 44
  11. 2022Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxelscited by 42
  12. 2022Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentationcited by 37
  13. 2023Self-Calibrated Cross Attention Network for Few-Shot Segmentationcited by 32
  14. 2022Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentationcited by 28
  15. 2023Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformercited by 28
  16. 2023A Systematic Review of Few-Shot Learning in Medical Imagingcited by 28
  17. 2022Q-Net: Query-Informed Few-Shot Medical Image Segmentationcited by 24
  18. 2022Mask Matching Transformer for Few-Shot Segmentationcited by 22
  19. 2023DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalizationcited by 21
  20. 2023RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Networkcited by 17
  21. 2021Few-Shot Domain Adaptation with Polymorphic Transformerscited by 14
  22. 2022Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registrationcited by 14
  23. 2024Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformationcited by 13
  24. 2024Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentationcited by 13
  25. 2023Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentationcited by 9
  26. 2022Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentationcited by 7
  27. 2023Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignmentcited by 7
  28. 2021Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Imagescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentationcited by 8
  2. 2025Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentationcited by 1
  3. 2026Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Promptingcited by 1
  4. 2026Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentationcited by 0
  5. 2026PSP: Harnessing Position and Shape Priors for Cross-Domain Few-Shot Medical Image Segmentationcited by 0
  6. 2026From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysiscited by 0