Few-Shot Medical Image Segmentation

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Few-shot medical image segmentation studies how to segment new structures or imaging settings when only a few labeled examples are available. Common approaches compare labeled support images with a query image using prototypes or meta-learning, while newer methods adapt foundation models through prompts, lightweight adapters, or LoRA. Shape and position priors, registration, and self-supervised features can help when anatomy or image appearance differs between support and query cases. Performance is improving, but reliability under domain shift still depends heavily on support selection and prompt quality, and systems can struggle to recognize when a target is absent.

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42 papers3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

MedAdaptAgent: An Automatic Harness for Adapting Medical Imaging Models to New Sites
Oct 26, 2026Hanxue Gu, Zhonghao Liu, Xingrui Gu +2Unsupervised Domain Adaptation Medical Image Segmentation
University of California, San Francisco · University of California, Berkeley
A General-Purpose Dual-Path Interaction Framework for Multi-View Medical Image Segmentation: Alleviating Data Volume Dependency in Small-Sample Regimes
Oct 22, 2026Shuhuan Wang, Yuanyuan Cui, Pengju Wang +10Medical Image Segmentation Few-Shot Medical Image Segmentation
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110169, China · Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, Liaoning, 110000, China · Department of Cardiology, Qilu Hospital of Shandong University +1
Performance at What Cost? A Sustainability-Aware Performance Index for Cell and Nucleus Instance Segmentation
Oct 7, 2026Eiram Mahera Sheikh, Alaa Tharwat, Wolfram SchenckInstance Segmentation Cost-Aware Inference
Department of Engineering and Mathematics, Bielefeld University of Applied Sciences and Arts, Interaktion 1, 33619 Bielefeld
FSS-UBrain: Multi-region Few-Shot Brain Tumor MRI Segmentation
Sep 26, 2026Truong Viet Vu, Nguyen Phuc Nguyen, Dang Thi Thu Hang +4Medical Image Segmentation Few-Shot Medical Image Segmentation
Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City 70000, Vietnam · Institute of Information Technology and Electrical-Electronics Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City 70000, Vietnam
Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings
Sep 16, 2026Chao Qin, Fahad Shahbaz Khan, Salman Khan +4Interactive Medical Image Segmentation Ultrasound Image Segmentation
Mohamed bin Zayed Uni. of Artificial Intelligence, Abu Dhabi, UAE. · Sheikh Tahnoon Bin Mohammed Medical City (STMC), Al Ain, UAE. · King’s College Hospital London - Dubai, Dubai, UAE. +1
From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
Sep 9, 2026Yazhou ZhuInteractive Medical Image Segmentation Selective Prediction
Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applications
Sep 1, 2026S. Sifaoui, E. Angelini, S. Toupin +2Representation Learning Visual Representation Learning
LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France · MIRACL.ai Laboratory, Hôpital Universitaire Lariboisière (AP-HP), Paris, France · Université Paris Cité, Inserm MASCOT, Paris, France
A Few Cases Are All You Need: An Empirical Study of Annotation-Efficient LoRA Fine-Tuning of MedSAM3
Aug 19, 2026Sachin Dudda Nagaraju, Bendik Skarre Abrahamsen, Ashkan Moradi +1Few-Shot Medical Image Segmentation 3D Medical Image Segmentation
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway · Central Staff, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
H2AL: Hyperbolic Hierarchy-aware Aggregative Learning for Registration-based Few-shot Medical Image Segmentation
Aug 7, 2026Jia Wang, Jiaming Cai, Zunying Hu +4Hierarchical Representation Learning Medical Image Registration
Dalian University of Technology Dalian, China · Beijing Children’s Hospital, Capital Medical University Beijing, China · Chongqing University of Posts and Telecommunications Chongqing, China
Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation
Aug 6, 2026Abbas Al-Sabbagh, Shalom F. Mushtaq, Tomás M. da Silva +7Ensemble Learning Medical Image Segmentation
Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom · Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom · Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom +1
Few-Shot Concept Prompt Learning for Segmentation Foundation Models via Visual Grounding
Aug 3, 2026Rahul Venkataramani, Rachana SathishMedical Imaging Foundation Models Promptable Image Segmentation
Advanced Technology Group, GE HealthCare
Benchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation
Jul 30, 2026Jinghong Liu, Yuchuan Deng, Fanping Liu +2Medical Imaging Foundation Models Few-Shot Medical Image Segmentation
Renmin University of China
Bi-Level Collaborative Learning for Few-Shot Scribble-Supervised Medical Image Segmentation
Jul 28, 2026Xiang-Xiang Su, Yufan Ye, Yihang Zheng +2Weakly Supervised Image Segmentation Semi-Supervised Medical Image Segmentation
Fuzhou University Fuzhou, China · Qingdao University Qingdao, China
Active few-shot segmentation by reinforcing data selection
Jul 24, 2026Chenlan Zhao, Benny Wong, Timothy F. Lundberg +8Active Learning Medical Image Segmentation
Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom · Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom · UCL Hawkes Institute; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom +2
RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation
Jul 6, 2026Dongyi He, Xiangkai Wang, Binbing Xu +5Few-Shot Medical Image Segmentation Segmentation Uncertainty Estimation
Department of Language Science and Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China · School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China · Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China +1
PSP: Harnessing Position and Shape Priors for Cross-Domain Few-Shot Medical Image Segmentation
Jun 27, 2026Bin Xu, Yazhou Zhu, Haofeng ZhangCross-Modality Medical Image Segmentation Medical Image Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation
Jun 25, 2026Quan Zhou, Shaoqing Zhai, Qiang Hu +3Medical Imaging Foundation Models Test-Time Adaptation
Wuhan University of Technology · Huazhong University of Science and Technology · Changzhou United lmaging Healthcare Surgical Technology Co. Ltd.
Vision-Language Models as Zero-Annotation Oracles in Histopathology
Jun 15, 2026Vishal Jain, Giorgio Buzzanca, Sarah Cechnicka +6Computational Pathology Histopathology Image Segmentation
Imperial College London, London, UK · Leiden University Medical Center, Leiden, The Netherlands · KU Leuven, Leuven, Belgium +3
Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentation
Jun 15, 2026Truong Vu, Minh Khoi Ho, Yutong XiePrototype-Based Learning Inter-Rater Reliability
Mohamed bin Zayed University of Artificial Intelligence
Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images
Jun 14, 2026Changwoo SongVision Foundation Model Adaptation Medical Image Segmentation
Oncosoft Inc., Seoul, South Korea · Department of Computer Science & Engineering, Chungnam National University, Daejeon, South Korea
MuellerPT: Decomposition Driven Pretraining for Dense Learning in Mueller Polarimetry
May 22, 2026Adam Tlemsani, Yingdian Li, Maxime Giot +4Self-Supervised Pre-Training Medical Image Classification
Department of Computing, Imperial College London · Hamlyn Centre for Robotic Surgery, Imperial College London · Department of Surgery and Cancer, Imperial College London +2
Benchmarking transferability of SSL pretraining to same and different modality segmentation tasks
May 18, 2026Jue Jiang, Harini VeeraraghavanCross-Modality Medical Image Segmentation Self-Supervised Pre-Training
Department of Medical Physics, Memorial Sloan Kettering Cancer Center
Beyond Euclidean Prototypes: Spectral Disentanglement and Geodesic Matching for Few-Shot Medical Image Segmentation
May 18, 2026Penghao Jia, Zhiyong Huang, Mingyang Hou +4Prototype-Based Learning Image Segmentation
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China · Health Management Institute, The Second Medical Center, and the National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentation
May 11, 2026Feifan Song, Yuntian Bo, Haofeng ZhangCross-Modality Medical Image Segmentation Image Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology
GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks
May 11, 2026Hantao Zhang, Weidong Guo, Yuhe Liu +5Cross-Modality Medical Image Segmentation Diffusion Model Guidance
CVLab, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland · Fudan University, Shanghai, China · Beihang University, Beijing, China +3
DINO-MVR: Multi-View Readout of Frozen DINOv3 for Annotation-Efficient Medical Segmentation
May 8, 2026Wei Jiang, Feng Liu, Nan Ye +1Image Segmentation Medical Imaging Foundation Models
The University of Queensland · The University of Newcastle
DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation
Apr 27, 2026Tal Grossman, Noa Cahan, Lev Ayzenberg +1Diffusion Models Image Segmentation
School of Electrical Engineering, Tel Aviv University, Israel · School of Biomedical Engineering, Tel Aviv University, Israel
SemiSAM-O1: How far can we push the boundary of annotation-efficient medical image segmentation?
Apr 27, 2026Yichi Zhang, Le Xue, Bichun Xu +6Image Segmentation Medical Imaging Foundation Models
Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China. · Shanghai Academy of Artificial Intelligence for Science, Shanghai, China. · Department of Radiotherapy, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China. +2
DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
Apr 25, 2026Niamh Belton, Victoria Joppin, Aonghus Lawlor +4Image Segmentation Magnetic Resonance Imaging
School of Medicine, University College Dublin, Dublin, Ireland. · Science Foundation Ireland Centre for Research Training in Machine2026 Learning, Dublin, Ireland. · Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille France. +4
Is SAM3 ready for pathology segmentation?
Apr 20, 2026Qiuyu Kong, Shakiba Sharifi, Yiming Wang +2Computational Pathology Image Segmentation
Sapienza University of Rome · University of Verona · Fondazione Bruno Kessler

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MedAdaptAgent: An Automatic Harness for Adapting Medical Imaging Models to New Sites

Hanxue Gu, Zhonghao Liu, Xingrui Gu, Kang Wang+1

University of California, San Francisco · University of California, Berkeley

Unsupervised Domain Adaptation Medical Image Segmentation LLM Agents Medical Image Classification Few-Shot Medical Image Segmentation

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Reusing pretrained medical-imaging models offers an alternative to training from scratch. A model trained for the same task may perform poorly at a new hospital because imaging protocols and preprocessing differ. Effective adaptation depends on the model architecture, domain shift, and available labels and compute. Adaptation can improve scores on a small labeled set yet reduce performance on unseen data. We present MedAdaptAgent, an automatic harness that turns a supplied model, target data, and a free-text request into a new-site adaptation workflow. Its LLM-driven Adaptation Planner uses model-site evidence to choose compatible experiments and revise subsequent adjustments. The harness requires evidence of improvement over the pretrained model, then delivers the selected model as a complete, runnable package. Evaluation spans 26 segmentation and classification transfers across architectures and compute settings, with segmentation annotation budgets ranging from zero-shot transfer to few-shot adaptation. With 20 local annotations, mean segmentation Dice rises from 0.483 to 0.774 across 10 transfers, a 60.1% relative gain, with no harmful replacements. Mean classification AUROC rises from 0.810 to 0.837 across 13 transfers, a 3.4% relative gain. Comparing adapted models with the pretrained model reduces harmful classification replacements by 30.8% versus selecting the highest-scoring model on the available labels, using identical candidates. Seven public-model deployments demonstrate adaptation and reproducible package delivery across contrast and modality shifts. A CT-to-MRI study extends the evaluation to four LLM planners, including two open-weight models. We release the code on GitHub.