Medical Imaging Foundation Models

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24 papers in the last four weeks, up 167% on the four weeks before. 0.2% of all new papers.

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OphMAE: Bridging Volumetric and Planar Imaging with a Foundation Model for Adaptive Ophthalmological Diagnosis
May 4, 2026Tienyu Chang, Zhen Chen, Renjie Liang +9Medical Imaging Foundation Models Masked Autoencoders
Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN · Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong +5
Representation learning from OCT images
May 4, 2026Hedi Tabia, Désiré Sidibé, Nawres Khlifa +6Medical Imaging Medical Imaging Foundation Models
IBISC Univ. Evry Université Paris-Saclay · University of Tunis El Manar · ESIEE Paris - Université Gustave Eiffel +2
GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models
May 3, 2026Favour Nerrise, Lucy Yin, Mohammad H. Abbasi +2Alzheimer's Disease Medical Imaging Foundation Models
Stanford University · Stanford University, CA
BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning
Apr 30, 2026Yizhou Wu, Shansong Wang, Yuheng Li +5Medical Imaging Foundation Models Self-Supervised Pre-Training
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA. · Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL, USA. · Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. +2
Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment
Apr 28, 2026Sanghati BasuDistribution Shift Robustness Image Segmentation
Healthcare Informatics, University of Illinois Springfield
DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation
Apr 27, 2026Tal Grossman, Noa Cahan, Lev Ayzenberg +1Source-Free Domain Adaptation Image Segmentation
School of Electrical Engineering, Tel Aviv University, Israel · School of Biomedical Engineering, Tel Aviv University, Israel
EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT
Apr 27, 2026Xuguang Bai, Mingxuan Liu, Tongxi Song +6Medical Imaging Foundation Models Radiology VLMs
School of Biomedical Engineering, Tsinghua University, Beijing, China. · Department of Radiology, Mianyang Central Hospital, Mianyang, China. · Center for Biomedical Imaging Research, Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing, China, 100084
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
CRC-SAM: SAM-Based Multi-Modal Segmentation and Quantification of Colorectal Cancer in CT, Colonoscopy, and Histology Images
Apr 25, 2026Daniel LaoCross-Modality Medical Image Segmentation Image Segmentation
Independent researcher
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM
Apr 25, 2026Jingxuan Kang, Ziqi Zhang, Shaoming Zheng +9Medical Imaging Foundation Models Promptable Image Segmentation
Imperial College London · Beihang University · National Health Service +1
Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
Apr 23, 2026Deepank Girish, Yi Hao Chan, Sukrit Gupta +2Medical Imaging Foundation Models Functional Connectivity
College of Computing and Data Science, Nanyang Technological University, Singapore
SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation
Apr 19, 2026Yihong Yao, Chunlei Li, Canxuan Gang +4Image Segmentation Medical Imaging Foundation Models
AI Geeks · Qingdao Municipal Hospital · University of Chinese Academy of Sciences
SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation
Apr 19, 2026Zequan Yao, Zixuan Tang, Jielin Ma +1Image Segmentation Medical Imaging Foundation Models
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen China.
Unified Ultrasound Intelligence Toward an End-to-End Agentic System
Apr 18, 2026Chen Ma, Yunshu Li, Junhu Fu +3Medical Imaging Foundation Models Multi-Task Learning
College of Biomedical Engineering, Fudan University, Shanghai, China
Beyond Independent Frames: Latent Attention Masked Autoencoders for Multi-View Echocardiography
Apr 16, 2026Simon Böhi, Irene Cannistraci, Sergio Muñoz Gonzalez +8Medical Imaging Foundation Models Masked Autoencoders
Department of Biomedical Engineering, University of Basel, Switzerland · Department of Computer Science, ETH Zurich, Switzerland
Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining
Apr 14, 2026Junfeng Xia, Wenhao Ye, Xuanye Pan +3Medical Imaging Foundation Models Diffusion Transformer
Department of Biomedical Engineering, Southern University of Science and Technology, China · School of Biomedical Engineering, Shenzhen University, China
Towards Interpretable Foundation Models for Retinal Fundus Images
Mar 19, 2026Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi +1Medical Imaging Medical Imaging Foundation Models
Hertie Institute for AI in Brain Health, University of Tübingen, Germany · Tübingen AI Center, University of Tübingen, Germany
LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation
Mar 18, 2026Mohammad Robaitul Islam Bhuiyan, Sheethal Bhat, Melika QahqaieImage Segmentation Medical Imaging Foundation Models
Pattern Recognition Lab, Friedrich-Alexander-Universität, Erlangen, Germany · Siemens Healthineers, Erlangen, Germany
GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training
Mar 6, 2026Yingtai Li, Shuai Ming, Qiuli Wang +13Medical Imaging Foundation Models Radiology VLMs
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, China · Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, 215123, China · Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, USTC, Hefei, Anhui, 230001, China +7
Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language
Mar 3, 2026Michelle Stegeman, Lena Philipp, Fennie van der Graaf +20Computational Pathology Medical Imaging Foundation Models
and on behalf of the UNICORN consortium · Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands · Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands +2
Scalable next-scale autoregression for medical image generation across anatomical regions
Feb 16, 2026Zhicheng He, Yunpeng Zhao, Junde Wu +7Medical Imaging Foundation Models Healthcare
National University of Singapore · University of Oxford · Zhejiang University +1
TotalFM: An Organ-Separated 3D-CT Foundation Model Leveraging Large-Scale Routine Clinical Radiology Data
Jan 1, 2026Kohei Yamamoto, Tomohiro Kikuchi3D Foundation Models Medical Imaging Foundation Models
Department of Radiology, Jichi Medical University · Data Science Center, Jichi Medical University
Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation
Dec 20, 2025Ziyu Zhang, Yi Yu, Simeng Zhu +4Image Segmentation Medical Imaging Foundation Models
Nanjing University · The Ohio State University · Stanford University
MedSAM3: Delving into Segment Anything with Medical Concepts
Nov 24, 2025Anglin Liu, Xu R. Cao, Yifan Shen +4Image Segmentation Medical Imaging Foundation Models
The Hong Kong University of Science and Technology (Guangzhou) · University of Illinois Urbana-Champaign · Southeast University +1
Scaling Vision Transformers for Functional MRI with Flat Maps
Oct 15, 2025Connor Lane, Mihir Tripathy, Leema Krishna Murali +15Medical Imaging Foundation Models Masked Autoencoders
MedARC · Sophont · Baylor College of Medicine +3
Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection
Oct 8, 2025Franco Javier Arellano, José Ignacio OrlandoMedical Imaging Foundation Models Vision Foundation Model Adaptation
Yatiris Group, PLADEMA Institute, UNICEN, Tandil, Argentina · CONICET, Tandil, Argentina
FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
Aug 24, 2025Zhihao Chen, Qi Gao, Zilong Li +4Diffusion Models for Image Restoration Medical Imaging Foundation Models
Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China · Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China · School of Cyber Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China +1
Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets
May 27, 2025Alexander Jaus, Zdravko Marinov, Constantin Seibold +4Image Segmentation Medical Imaging Foundation Models
Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Tasks
May 6, 2025Haonan Wang, Jiaji Mao, Lehan Wang +11Image Segmentation Medical Imaging Foundation Models
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR · Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR
Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers
Apr 22, 2025Meng Wang, Tian Lin, Qingshan Hou +69Medical Imaging Foundation Models Medical Image Analysis

May 4, 2026, cs.CVJ/K move · Enter open · S save

OphMAE: Bridging Volumetric and Planar Imaging with a Foundation Model for Adaptive Ophthalmological Diagnosis

Tienyu Chang, Zhen Chen, Renjie Liang, Jinyu Ding+8

Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN · Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong+5

Medical Imaging Foundation Models Masked Autoencoders Medical Diagnosis Medical Image Classification Multimodal Fusion Medical Image Representation Learning

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The advent of foundation models has heralded a new era in medical artificial intelligence (AI), enabling the extraction of generalizable representations from large-scale unlabeled datasets. However, current ophthalmic AI paradigms are predominantly constrained to single-modality inference, thereby creating a dissonance with clinical practice where diagnosis relies on the synthesis of complementary imaging modalities. Furthermore, the deployment of high-performance AI in resource-limited settings is frequently impeded by the unavailability of advanced three-dimensional imaging hardware. Here, we present the Ophthalmic multimodal Masked Autoencoder (OphMAE), a multi-imaging foundation model engineered to synergize the volumetric depth of 3D Optical Coherence Tomography (OCT) with the planar context of 2D en face OCT. By implementing a novel cross-modal fusion architecture and a unique adaptive inference mechanism, OphMAE was pre-trained on a massive dataset with of 183,875 paired OCT images derived from 32,765 patients. In a rigorous benchmark encompassing 17 diverse diagnostic tasks with 48,340 paired OCT images from 8,191 patients, the model demonstrated state-of-the-art performance, achieving an Area Under the Curve (AUC) of 96.9% for Age-related Macular Degeneration (AMD) and 97.2% for Diabetic Macular Edema (DME), consistently surpassing existing single-modal and multimodal foundation models. Crucially, OphMAE exhibits robust engineering adaptability: it maintains high diagnostic accuracy, such as 93.7% AUC for AMD, even when restricted to single-modality 2D inputs, and demonstrates exceptional data efficiency by retaining 95.7% AUC with as few as 500 labeled samples. This work establishes a scalable and adaptable framework for ophthalmic AI, ensuring robust performance across different tasks.

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