Medical Imaging Foundation Models

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Overview

Medical imaging foundation models aim to learn reusable features from large collections of images, scans, and sometimes paired clinical reports, then apply them to tasks such as diagnosis, segmentation, measurement, and report generation. Common approaches include self-supervised learning, masked-image prediction, vision-language contrastive learning, and generative models, with both 2D and volumetric methods for modalities such as radiology, MRI, ultrasound, pathology, and retinal imaging. Researchers adapt these models through fine-tuning, lightweight adapters such as LoRA, frozen-feature probes, or prompts, and assess them on classification, segmentation, and cross-center benchmarks. A central tension is whether broad pretraining reliably helps specific clinical tasks: results vary by modality and task, and robustness to shifts in scanners, populations, and institutions remains essential to establish before deployment.

Latest papers

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235 papers21 papers in the last four weeks, down 28% on the four weeks before. 0.1% of all new papers.

  1. Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge

    Apr 13, 2026Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81Medical Imaging Foundation ModelsSelf-Supervised Pre-Training

  2. RobustMedSAM: Degradation-Resilient Medical Image Segmentation via Robust Foundation Model Adaptation

    Apr 10, 2026Jieru Li, Matthew Chen, Micky C. Nnamdi +3Medical Image SegmentationMedical Imaging Foundation Models

    Georgia Institute of Technology

  3. M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model

    Apr 10, 2026Yihang Liu, Longzhen Yang, Jiaxiong Yang +3Medical Imaging Foundation ModelsMedical Image Representation Learning

    School of Computer Science and Technique, Tongji University, Shanghai 201804, China. · School of Communications and Electronic Engineering, East China Normal University, Shanghai 200241, China.

  4. Adapting Foundation Models for Annotation-Efficient Adnexal Mass Segmentation in Cine Images

    Apr 9, 2026Francesca Fati, Alberto Rota, Adriana V. Gregory +9Medical Image SegmentationUltrasound Image Segmentation

    Mayo Clinic · Politecnico di Milano · Istituto Europeo di Oncologia

  5. Weight Group-wise Post-Training Quantization for Medical Foundation Model

    Apr 9, 2026Yineng Chen, Peng Huang, Aozhong Zhang +9Post-Training QuantizationLow-Bit Quantization

    University at Albany, SUNY · Southwest Jiaotong University · Purdue University +1

  6. Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis

    Apr 7, 2026Peixi Peng, Housheng Xie, Yanling Wei +5Medical Imaging Foundation ModelsMedical Diagnosis

    Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University · Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. · Daping Hospital, Army Medical University +3

  7. MedGemma 1.5 Technical Report

    Apr 6, 2026Andrew Sellergren, Chufan Gao, Fereshteh Mahvar +39Medical Imaging Foundation Models3D Medical Imaging

  8. Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks

    Apr 5, 2026Rubén Moreno-Aguado, Alba Magallón, Victor Moreno +2Medical Imaging Foundation ModelsMedical Image Representation Learning

    Bioengineering Department and Imperial-X, Imperial College London, London, UK · Department of Computer Science, University of Manchester, Manchester, UK · Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain +6

  9. Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models

    Apr 2, 2026Antoine Saporta, Baptiste Callard, Corentin Dancette +5Medical Imaging Foundation ModelsMedical Imaging

    Raidium, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France. · Department of Vascular and Oncological Interventional Radiology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France · Faculté de Santé, Université Paris-Cité, Paris, France.

  10. AdaLoRA-QAT: Adaptive Low-Rank and Quantization-Aware Segmentation

    Apr 1, 2026Prantik Deb, Srimanth Dhondy, N. Ramakrishna +3Medical Image SegmentationMedical Imaging Foundation Models

    International Institute of Information Technology (IIIT-H), Hyderabad, India · Nizam’s Institute of Medical Sciences (NIMS), Hyderabad, India · The Alan Turing Institute, London and University College London, United Kingdom

  11. Segmentation of Gray Matters and White Matters from Brain MRI data

    Mar 31, 2026Chang Sun, Rui Shi, Tsukasa Koike +3Medical Image SegmentationMedical Imaging Foundation Models

    Department of Computer Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan · Department of Neurosurgery, Teraoka Memorial Hospital, Fukuyama, Hiroshima, Japan · Department of Radiology, Nippon Medical School Hospital, Tokyo, Japan +1

  12. DinoDental: Benchmarking DINOv3 as a Unified Vision Encoder for Dental Image Analysis

    Mar 30, 2026Kun Tang, Xinquan Yang, Mianjie Zheng +6Medical Image AnalysisVision Foundation Model Adaptation

    Shenzhen University. · Shenzhen University General Hospital. · Chongqing University.

  13. RAP: Retrieve, Adapt, and Prompt-Fit for Training-Free Few-Shot Medical Image Segmentation

    Mar 29, 2026Zhihao Mao, Bangpu ChenMedical Image SegmentationFew-Shot Medical Image Segmentation

    School of Computer Science, China University of Geosciences, Wuhan, China

  14. Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

    Mar 29, 2026Zhongying Deng, Cheng Tang, Ziyan Huang +124Medical ImagingMedical Imaging Foundation Models

  15. Adapting Segment Anything Model 3 for Concept-Driven Lesion Segmentation in Medical Images: An Experimental Study

    Mar 26, 2026Guoping Xu, Jayaram K. Udupa, Yubing Tong +5Medical Image SegmentationPromptable Image Segmentation

    The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA · Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA · Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

  16. Sparse Autoencoders for Interpretable Medical Image Representation Learning

    Mar 24, 2026Philipp Wesp, Robbie Holland, Vasiliki Sideri-Lampretsa +1Medical Image RetrievalExplainable Medical Image Analysis

    Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, CA, USA · Department of Radiology, Stanford University, Stanford, CA, USA · Chair of AI in Healthcare and Medicine, Technical University of Munich, Munich, Germany +1

  17. Bi-CRCL: Bidirectional Conservative-Radical Complementary Learning with Pre-trained Foundation Models for Class-incremental Medical Image Analysis

    Mar 24, 2026Xinyao Wu, Zhe Xu, Cheng Chen +3Class-Incremental LearningMedical Image Analysis

    Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China. · Department of Radiation Oncology, Columbia University Irving Medical Center and Data Science Institute, Columbia University, New York, NY, USA. · Department of Electrical and Electronic Engineering and School of Biomedical Engineering, The University of Hong Kong, Hong Kong, China. +2

  18. Automatic Segmentation of 3D CT scans with SAM2 using a zero-shot approach

    Mar 24, 2026Miquel Lopez Escoriza, Pau Amargant Alvarez3D Medical Image SegmentationZero-Shot Medical Image Segmentation

    Department of Computer Science, EPFL, Switzerland

  19. Towards Interpretable Foundation Models for Retinal Fundus Images

    Mar 19, 2026Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi +1Fundus ImagingMedical Imaging Foundation Models

    Hertie Institute for AI in Brain Health, University of Tübingen, Germany · Tübingen AI Center, University of Tübingen, Germany

  20. Understanding Task Aggregation for Generalizable Ultrasound Foundation Models

    Mar 18, 2026Fangyijie Wang, Tanya Akumu, Vien Ngoc Dang +5Medical Imaging Foundation ModelsMulti-Task Learning

    Research Ireland Centre for Research Training in Machine Learning · School of Medicine, University College Dublin, Dublin, Ireland · Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain +4

  21. DeepCORO-CLIP: A Multi-View Foundation Model for Comprehensive Coronary Angiography Video-Text Analysis and External Validation

    Mar 18, 2026Sarra Harrabi, Yichen Wu, Geoffrey H. Tison +6Coronary AngiographyMedical Imaging Foundation Models

    Division of Cardiology, Department of Medicine, Montreal Heart Institute, 5000 Belanger Street, QC, H1T 1C8 Canada · HeartWise.ai, 5000 Belanger Street, QC, H1T 1C8 Canada · McGill, Faculty of Engineering, Montreal, Quebec, Canada. +7

  22. LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation

    Mar 18, 2026Mohammad Robaitul Islam Bhuiyan, Sheethal Bhat, Melika Qahqaie +4Image SegmentationMedical Imaging Foundation Models

    Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany · Siemens Healthineers, Erlangen, Germany

  23. Adaptation of Foundation Models in Medical Image Analysis: Mechanisms, Evidence, and Deployment Considerations

    Mar 15, 2026Karma Phuntsho, Abdullah, Kyungmi Lee +2Medical Imaging Foundation ModelsVision Foundation Model Adaptation

    James Cook University, 14-88 McGregor Rd, Smithfield, 4878, Queensland, Australia

  24. Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision

    Mar 14, 2026Yunhe Gao, Yabin Zhang, Chong Wang +5Medical Image Representation LearningSelf-Supervised Pre-Training

    Stanford University

  25. SAIF: A Stability-Aware Inference Framework for Medical Image Segmentation with Segment Anything Model

    Mar 13, 2026Ke Wu, Shiqi Chen, Yiheng Zhong +5Medical Image SegmentationPromptable Image Segmentation

    Xi’an Jiaotong-Liverpool University, Suzhou, China · University College London, London, United Kingdom · Anhui Jianzhu University, Hefei, China

  26. Developing Foundation Models for Universal Segmentation from 3D Whole-Body Positron Emission Tomography

    Mar 12, 2026Yichi Zhang, Le Xue, Wenbo Zhang +16Medical Imaging Foundation Models3D Medical Image Segmentation

    Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China. · Shanghai Academy of Artificial Intelligence for Science, Shanghai, China. · Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China. +6

  27. Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI

    Mar 11, 2026Joan Perramon-Llussà, Amelia Jiménez-Sánchez, Grzegorz Skorupko +4Federated Fine-TuningMedical Imaging Foundation Models

    Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain · Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

  28. Prompting with the human-touch: evaluating model-sensitivity of foundation models for musculoskeletal CT segmentation

    Mar 11, 2026Caroline Magg, Maaike A. ter Wee, Johannes G. G. Dobbe +4Medical Image SegmentationMedical Imaging Foundation Models

    Quantitative Healthcare Analysis (QurAI) Group, University of Amsterdam, Science Park 900, Amsterdam, 1098 XH, The Netherlands · Department Biomedical Engineering and Physics, Amsterdam UMC, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands · Department Orthopaedics, Amsterdam UMC, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands

  29. DINOv3 with Test-Time Calibration for Automated Carotid Intima-Media Thickness Measurement on CUBS v1

    Mar 11, 2026Zhenpeng Zhang, Jinwei Lu, Yurui Dong +1Medical ImagingUltrasound Image Segmentation

    School of Basic Medical Sciences, Fudan University, Shanghai 200032, China · Fudan University, Shanghai, China · Institute of Medical Genetics and Genomics, Fudan University, Shanghai 200032, China

  30. MIL-PF: Multiple Instance Learning on Precomputed Features for Mammography Classification

    Mar 10, 2026Nikola Jovišić, Milica Škipina, Nicola Dall'Asen +1Mammography Image ClassificationMedical Image Classification

    Institute for AI R&D of Serbia, Serbia · Faculty of Technical Sciences, University of Novi Sad, Serbia · University of Trento, Italy +2