# Few Shot Medical Image Segmentation with Cross Attention Transformer

arXiv: https://arxiv.org/abs/2303.13867 · HTML: https://mlsanity.com/paper/2303.13867/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 30 of 31)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Prior Guided Feature Enrichment Network for Few-Shot Segmentation](https://mlsanity.com/paper/2008.01449/) (2020)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Few-Shot Segmentation via Cycle-Consistent Transformer](https://mlsanity.com/paper/2106.02320/) (2021)
- [An Overview of Deep Semi-Supervised Learning](https://mlsanity.com/paper/2006.05278/) (2020)
- [Learning What Not to Segment: A New Perspective on Few-Shot Segmentation](https://mlsanity.com/paper/2203.07615/) (2022)
- [Self-Support Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2207.11549/) (2022)
- ['Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images](https://mlsanity.com/paper/1902.01314/) (2019)
- [Recurrent Mask Refinement for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2108.00622/) (2021)
- [Generalized Few-shot Semantic Segmentation](https://mlsanity.com/paper/2010.05210/) (2020)
- [Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding](https://mlsanity.com/paper/2012.05440/) (2020)
- [Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels](https://mlsanity.com/paper/2203.02048/) (2022)
- [Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2210.06780/) (2022)
- [PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask](https://mlsanity.com/paper/2109.13788/) (2021)
- [All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.13930/) (2021)
- [Q-Net: Query-Informed Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2208.11451/) (2022)
- [Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement](https://mlsanity.com/paper/2207.04183/) (2022)
- [Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression](https://mlsanity.com/paper/2208.12462/) (2022)
- [Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation](https://mlsanity.com/paper/2211.14491/) (2022)
- [Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning](https://mlsanity.com/paper/2201.06358/) (2022)

## Built on by (most cited first, 19 of 19)

- [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024)
- [Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models](https://mlsanity.com/paper/2408.08813/) (2024)
- [Recent Advances in Medical Imaging Segmentation: A Survey](https://mlsanity.com/paper/2505.09274/) (2025)
- [Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching](https://mlsanity.com/paper/2503.04826/) (2025)
- [Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2505.21897/) (2025)
- [Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation](https://mlsanity.com/paper/2512.18176/) (2025)
- [Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion](https://mlsanity.com/paper/2408.14427/) (2024)
- [Few-Shot Deployment of Pretrained MRI Transformers in Brain Imaging Tasks](https://mlsanity.com/paper/2508.05783/) (2025)
- [Dual Interaction Network with Cross-Image Attention for Medical Image Segmentation](https://mlsanity.com/paper/2509.05953/) (2025)
- [Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Prompting](https://mlsanity.com/paper/2603.21287/) (2026)
- [Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI](https://mlsanity.com/paper/2411.06911/) (2024)
- [FCC: Fully Connected Correlation for One-Shot Segmentation](https://mlsanity.com/paper/2411.11917/) (2024)
- [BOTM: Echocardiography Segmentation via Bi-directional Optimal Token Matching](https://mlsanity.com/paper/2505.18052/) (2025)
- [SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2604.03134/) (2026)
- [Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM](https://mlsanity.com/paper/2604.23314/) (2026)
- [Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2605.10885/) (2026)
- [Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2606.16325/) (2026)
- [PSP: Harnessing Position and Shape Priors for Cross-Domain Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2606.28799/) (2026)
- [FSS-UBrain: Multi-region Few-Shot Brain Tumor MRI Segmentation](https://mlsanity.com/paper/2609.32273/) (2026)

## Abstract (arXiv)

Medical image segmentation has made significant progress in recent years. Deep learning-based methods are recognized as data-hungry techniques, requiring large amounts of data with manual annotations. However, manual annotation is expensive in the field of medical image analysis, which requires domain-specific expertise. To address this challenge, few-shot learning has the potential to learn new classes from only a few examples. In this work, we propose a novel framework for few-shot medical image segmentation, termed CAT-Net, based on cross masked attention Transformer. Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and boosting the representation capacity of both the support prototype and query features. We further design an iterative refinement framework that refines the query image segmentation iteratively and promotes the support feature in turn. We validated the proposed method on three public datasets: Abd-CT, Abd-MRI, and Card-MRI. Experimental results demonstrate the superior performance of our method compared to state-of-the-art methods and the effectiveness of each component. Code: https://github.com/hust-linyi/CAT-Net.