# Few-shot Medical Image Segmentation with Cycle-resemblance Attention

arXiv: https://arxiv.org/abs/2212.03967 · HTML: https://mlsanity.com/paper/2212.03967/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 65 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [TADAM: Task dependent adaptive metric for improved few-shot learning](https://mlsanity.com/paper/1805.10123/) (2018)
- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2004.04581/) (2020)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Adaptive Prototype Learning and Allocation for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01893/) (2021)
- [SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation](https://mlsanity.com/paper/1810.09091/) (2018)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Few-Shot Segmentation via Cycle-Consistent Transformer](https://mlsanity.com/paper/2106.02320/) (2021)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- [Learning What Not to Segment: A New Perspective on Few-Shot Segmentation](https://mlsanity.com/paper/2203.07615/) (2022)
- [CARAFE: Content-Aware ReAssembly of FEatures](https://mlsanity.com/paper/1905.02188/) (2019)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [Self-Guided and Cross-Guided Learning for Few-Shot Segmentation](https://mlsanity.com/paper/2103.16129/) (2021)
- ['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)
- [Superpixels: An Evaluation of the State-of-the-Art](https://mlsanity.com/paper/1612.01601/) (2016)
- [CNN-based Segmentation of Medical Imaging Data](https://mlsanity.com/paper/1701.03056/) (2017)
- [Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2204.10638/) (2022)
- [An application of cascaded 3D fully convolutional networks for medical image segmentation](https://mlsanity.com/paper/1803.05431/) (2018)
- [Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis](https://mlsanity.com/paper/1812.07509/) (2018)
- [CARAFE++: Unified Content-Aware ReAssembly of FEatures](https://mlsanity.com/paper/2012.04733/) (2020)

## Built on by (most cited first, 30 of 33)

- [UniverSeg: Universal Medical Image Segmentation](https://mlsanity.com/paper/2304.06131/) (2023)
- [Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer](https://mlsanity.com/paper/2309.04825/) (2023)
- [Tyche: Stochastic In-Context Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.13650/) (2024)
- [Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning](https://mlsanity.com/paper/2308.16466/) (2023)
- [Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation](https://mlsanity.com/paper/2303.17051/) (2023)
- [ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models](https://mlsanity.com/paper/2407.07042/) (2024)
- [DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation](https://mlsanity.com/paper/2403.03273/) (2024)
- [Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2309.11172/) (2023)
- [RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning](https://mlsanity.com/paper/2409.04298/) (2024)
- [BEN: Using Confidence-Guided Matting for Dichotomous Image Segmentation](https://mlsanity.com/paper/2501.06230/) (2025)
- [Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach](https://mlsanity.com/paper/2307.14446/) (2023)
- [SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging](https://mlsanity.com/paper/2403.16578/) (2024)
- [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)
- [ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2401.14074/) (2024)
- [MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures](https://mlsanity.com/paper/2402.01031/) (2024)
- [Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation](https://mlsanity.com/paper/2402.04958/) (2024)
- [SPENet: Self-guided Prototype Enhancement Network for Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2509.02993/) (2025)
- [Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion](https://mlsanity.com/paper/2408.14427/) (2024)
- [Few-Shot Medical Image Segmentation with Large Kernel Attention](https://mlsanity.com/paper/2407.19148/) (2024)
- [Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images](https://mlsanity.com/paper/2408.06235/) (2024)
- [Hierarchical Cross-Attention Network for Virtual Try-On](https://mlsanity.com/paper/2411.15542/) (2024)
- [DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark](https://mlsanity.com/paper/2604.23187/) (2026)
- [Few-shot Medical Image Segmentation via Cross-Reference Transformer](https://mlsanity.com/paper/2304.09630/) (2023)
- [Toward Robust Canine Cardiac Diagnosis: Deep Prototype Alignment Network-Based Few-Shot Segmentation in Veterinary Medicine](https://mlsanity.com/paper/2403.06471/) (2024)
- [Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2405.07516/) (2024)
- [Few-Shot Medical Image Segmentation with High-Fidelity Prototypes](https://mlsanity.com/paper/2406.18074/) (2024)
- [Prototypical Few-Shot Medical Image Semantic Segmentation with Background Fusion](https://mlsanity.com/paper/2412.02983/) (2024)
- [A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains](https://mlsanity.com/paper/2504.04017/) (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)

## Abstract (arXiv)

Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing attention in the medical image semantic segmentation field. To perform segmentation with limited number of labeled medical images, most existing studies use Proto-typical Networks (PN) and have obtained compelling success. However, these approaches overlook the query image features extracted from the proposed representation network, failing to preserving the spatial connection between query and support images. In this paper, we propose a novel self-supervised few-shot medical image segmentation network and introduce a novel Cycle-Resemblance Attention (CRA) module to fully leverage the pixel-wise relation between query and support medical images. Notably, we first line up multiple attention blocks to refine more abundant relation information. Then, we present CRAPNet by integrating the CRA module with a classic prototype network, where pixel-wise relations between query and support features are well recaptured for segmentation. Extensive experiments on two different medical image datasets, e.g., abdomen MRI and abdomen CT, demonstrate the superiority of our model over existing state-of-the-art methods.