mlsanityData to 14 Oct 2026

Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation

arXiv 2309.11172 ↗First posted 20 Sep 2023cs.CVcited by 9

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Abstract (arXiv)

Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current mainstream methods primarily focus on extracting holistic representations from support images with large intra-class variations in appearance and background, and encounter difficulties in adapting to query images. In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions. Specifically, the foreground of the support image is decomposed into distinct regions, which are subsequently used to derive region-level representations via a designed Regional Prototypical Learning (RPL) module. We then introduce a novel Prototypical Representation Debiasing (PRD) module based on a two-way elimination mechanism which suppresses the disturbance of regional representations by a self-support, Multi-direction Self-debiasing (MS) block, and a support-query, Interactive Debiasing (ID) block. Finally, an Assembled Prediction (AP) module is devised to balance and integrate predictions of multiple prototypical representations learned using stacked PRD modules. Results obtained through extensive experiments on three publicly accessible medical imaging datasets demonstrate consistent improvements over the leading FSMIS methods. The source code is available at https://github.com/YazhouZhu19/PAMI.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 38 of its 64 references that are in the map. First posted on arXiv 20 Sep 2023.

Builds on

38 of 64 references.

References · 38

  1. 2017Attention Is All You Needcited by 60,361
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  11. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  12. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
26 more
  1. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 309
  2. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  3. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  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. 2020Part-aware Prototype Network for Few-shot Semantic Segmentationcited by 162
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  9. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  10. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  11. 2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
  12. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  13. 2022Self-Support Few-Shot Semantic Segmentationcited by 96
  14. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
  15. 2019'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Imagescited by 69
  16. 2021Mining Latent Classes for Few-shot Segmentationcited by 69
  17. 2021Recurrent Mask Refinement for Few-Shot Medical Image Segmentationcited by 63
  18. 2022Feature-Proxy Transformer for Few-Shot Segmentationcited by 58
  19. 2022Learning Non-target Knowledge for Few-shot Semantic Segmentationcited by 57
  20. 2020Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embeddingcited by 48
  21. 2022Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxelscited by 42
  22. 2022Few-shot Medical Image Segmentation with Cycle-resemblance Attentioncited by 33
  23. 2022Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentationcited by 28
  24. 2023Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformercited by 28
  25. 2022Q-Net: Query-Informed Few-Shot Medical Image Segmentationcited by 24
  26. 2022Mask Matching Transformer for Few-Shot Segmentationcited by 22

Built on by

Cited by year (2026 to 14 Oct)

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

9 papers.

  1. 2025MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentationcited by 8
  2. 2024RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentationcited by 6
  3. 2025SPENet: Self-guided Prototype Enhancement Network for Few-shot Medical Image Segmentationcited by 3
  4. 2025Tied Prototype Model for Few-Shot Medical Image Segmentationcited by 1
  5. 2025Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentationcited by 1
  6. 2026SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentationcited by 0
  7. 2026Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentationcited by 0
  8. 2026Beyond Euclidean Prototypes: Spectral Disentanglement and Geodesic Matching for Few-Shot Medical Image Segmentationcited by 0
  9. 2026From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysiscited by 0