mlsanityData to 14 Oct 2026

Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer

arXiv 2309.04825 ↗First posted 9 Sep 2023cs.CVcited by 28

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Abstract (arXiv)

Automated segmentation of large volumes of medical images is often plagued by the limited availability of fully annotated data and the diversity of organ surface properties resulting from the use of different acquisition protocols for different patients. In this paper, we introduce a more promising few-shot learning-based method named Region-enhanced Prototypical Transformer (RPT) to mitigate the effects of large intra-class diversity/bias. First, a subdivision strategy is introduced to produce a collection of regional prototypes from the foreground of the support prototype. Second, a self-selection mechanism is proposed to incorporate into the Bias-alleviated Transformer (BaT) block to suppress or remove interferences present in the query prototype and regional support prototypes. By stacking BaT blocks, the proposed RPT can iteratively optimize the generated regional prototypes and finally produce rectified and more accurate global prototypes for Few-Shot Medical Image Segmentation (FSMS). Extensive experiments are conducted on three publicly available medical image datasets, and the obtained results show consistent improvements compared to state-of-the-art FSMS methods. The source code is available at: https://github.com/YazhouZhu19/RPT.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 9 Sep 2023.

Builds on

16 of 24 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  4. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  5. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 309
  6. 2018Boundary loss for highly unbalanced segmentationcited by 190
  7. 2016Multivariate mixture model for myocardium segmentation combining multi-source imagescited by 133
  8. 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
  9. 2019'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Imagescited by 69
  10. 2022Feature-Proxy Transformer for Few-Shot Segmentationcited by 58
  11. 2020Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embeddingcited by 48
  12. 2022Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxelscited by 42
4 more
  1. 2022Few-shot Medical Image Segmentation with Cycle-resemblance Attentioncited by 33
  2. 2022Q-Net: Query-Informed Few-Shot Medical Image Segmentationcited by 24
  3. 2020Prototype Refinement Network for Few-Shot Segmentationcited by 11
  4. 2021PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learningcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 11; 2026: 11.

28 papers.

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  1. 2023Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentationcited by 9
  2. 2025SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Promptscited by 9
  3. 2025MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentationcited by 8
  4. 2024RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuningcited by 7
  5. 2024RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentationcited by 6
  6. 2025Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matchingcited by 4
  7. 2024ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentationcited by 3
  8. 2025Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentationcited by 3
  9. 2025SPENet: Self-guided Prototype Enhancement Network for Few-shot Medical Image Segmentationcited by 3
  10. 2025Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentationcited by 3
  11. 2024Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusioncited by 2
  12. 2025Tied Prototype Model for Few-Shot Medical Image Segmentationcited by 1
16 more
  1. 2025Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentationcited by 1
  2. 2026Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Promptingcited by 1
  3. 2026DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmarkcited by 1
  4. 2024Prototypical Few-Shot Medical Image Semantic Segmentation with Background Fusioncited by 0
  5. 2025LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentationcited by 0
  6. 2025Efficient Masked Attention Transformer for Few-Shot Classification and Segmentationcited by 0
  7. 2025AGENet: Adaptive Edge-aware Geodesic Distance Learning for Few-Shot Medical Image Segmentationcited by 0
  8. 2026RAP: Retrieve, Adapt, and Prompt-Fit for Training-Free Few-Shot Medical Image Segmentationcited by 0
  9. 2026SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentationcited by 0
  10. 2026DINO-MVR: Multi-View Readout of Frozen DINOv3 for Annotation-Efficient Medical Segmentationcited by 0
  11. 2026Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentationcited by 0
  12. 2026Beyond Euclidean Prototypes: Spectral Disentanglement and Geodesic Matching for Few-Shot Medical Image Segmentationcited by 0
  13. 2026Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentationcited by 0
  14. 2026PSP: Harnessing Position and Shape Priors for Cross-Domain Few-Shot Medical Image Segmentationcited by 0
  15. 2026H2AL: Hyperbolic Hierarchy-aware Aggregative Learning for Registration-based Few-shot Medical Image Segmentationcited by 0
  16. 2026From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysiscited by 0