Medical Imaging Foundation Models

Ask about this topic

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

CardsList

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

Mar 6, 2026

GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training

Radiology foundation models (RFMs) have largely inherited the scale-first recipe of natural-image vision--language pre-training. This recipe is difficult to deploy in 3D radiology, where training corpora are smaller, reports vary across institutions, and receiving hospitals often need local adaptation under privacy and compute constraints. We ask whether routine radiology reports can instead be converted into auditable diagnostic supervision that shapes the image encoder, text encoder, aligned space, and local-adaptation procedure. We develop GreenRFM, a supervision-centric pre-training framework organized around four empirical principles: More distilled, Ubiquitous, Semantic-enforcing, and Task-aligning (MUST) supervision. These principles convert noisy reports into structured diagnostic signals and use them to learn discriminative unimodal encoders plus an aligned image--text space for diagnosis-centered multimodal use. GreenRFM requires 24 GPU-hours on a single 24GB GPU (lightweight variant: 6GB VRAM, 4~hours) and reaches a zero-shot CT-RATE AUC of 84.8. Evaluations using more than 200,000 volumes from six institutions and two modalities show transfer to private clinical cohorts and to musculoskeletal MRI. On a local institutional cohort, computationally feasible retraining raises macro-AUC from 70.5 to 82.1. The aligned space also improves hepatocellular-carcinoma microvascular-invasion prediction and trans-arterial chemoembolization response analysis over established clinical scores. These results support supervision-centric pre-training as a practical route to resource-efficient, locally adaptable, diagnosis-centered radiology vision--language representations.

Yingtai Li, Shuai Ming, Qiuli Wang +13

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

Medical Imaging Foundation ModelsRadiology VLMsVision-Language ModelsVLM AdaptationMultimodal PretrainingEfficient VLM Training
AskPDF
Mar 6, 2026

Self-Auditing Parameter-Efficient Fine-Tuning for Few-Shot 3D Medical Image Segmentation

Adapting foundation models to new clinical sites remains challenging in practice. Domain shift and scarce annotations must be handled by experts, yet many clinical groups do not have ready access to skilled AI engineers to tune adapter designs and training recipes. As a result, adaptation cycles can stretch from weeks to months, particularly in few-shot settings. Existing PEFT methods either require manual adapter configuration or automated searches that are computationally infeasible in few-shot 3D settings. We propose SEA-PEFT (SElf-Auditing Parameter-Efficient Fine-Tuning) to automate this process. SEA-PEFT treats adapter configuration as an online allocation problem solved during fine-tuning rather than through manual, fixed-topology choices. SEA-PEFT uses a search-audit-allocate loop that trains active adapters, estimates each adapter's Dice utility by momentarily toggling it off, and then reselects the active set under a parameter budget using a greedy knapsack allocator. Exponential Moving Average and Interquartile Range smoothing, together with a Finite-State Ranking controller, stabilize the loop and improve reliability in high-noise few-shot regimes. On TotalSegmentator and FLARE'22, SEA-PEFT improves mean Dice by 2.4--2.8 points over the strongest fixed-topology PEFT baselines across 1/5/10-shot settings while training <1% of parameters. For reproducibility purposes, we made our code publicly available at https://github.com/tsly123/SEA_PEFT

Son Thai Ly, Hien V. Nguyen

University of Houston

Parameter-Efficient Fine-TuningFew-Shot Medical Image Segmentation3D Medical Image SegmentationFew-Shot Domain AdaptationMedical Imaging Foundation Models
AskPDF
Mar 4, 2026

ProFound: A moderate-sized vision foundation model for multi-task prostate imaging

Many diagnostic and therapeutic clinical tasks for prostate cancer increasingly rely on multi-parametric MRI. Automating these tasks is challenging because they necessitate expert interpretations, which are difficult to scale to capitalise on modern deep learning. Although modern automated systems achieve expert-level performance in isolated tasks, their general clinical utility remains limited by the requirement of large task-specific labelled datasets. In this paper, we present ProFound, a domain-specialised vision foundation model for volumetric prostate mpMRI. ProFound is pre-trained using several variants of self-supervised approaches on a diverse, multi-institutional collection of 5,000 patients, with a total of over 22,000 unique 3D MRI volumes (over 1,800,000 2D image slices). We conducted a systematic evaluation of ProFound across a broad spectrum of 1111 downstream clinical tasks on over 3,000 independent patients, including prostate cancer detection, Gleason grading, lesion localisation, gland volume estimation, zonal and surrounding structure segmentation. Experimental results demonstrate that finetuned ProFound consistently outperforms or remains competitive with state-of-the-art specialised models and existing medical vision foundation models trained/finetuned on the same data.

Yipei Wang, Yinsong Xu, Weixi Yi +11

UCL Hawkes Institute, University College London, London, UK · Department of Medical Physics and Biomedical Engineering, University College London, London, UK · School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China +11

Medical Imaging Foundation ModelsMedical Image Representation LearningMagnetic Resonance ImagingMedical Image SegmentationMedical Image Localization
AskPDF
Mar 3, 2026

Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language

Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data modalities, a single model can be rapidly adapted to address multiple modalities and tasks with minimal supervision. This potential comes with the urgent need to reliably benchmark, understand and compare the performance and clinical impact of foundation models across data modalities and clinical tasks. We introduce UNICORN, a fundamentally new benchmarking concept for medical foundation models. UNICORN brings four main contributions to medical artificial intelligence. First, a framework that enables a one-to-many benchmarking approach, where a single foundation model is tested across multiple tasks and data modalities. Here, we populate it with 20 tasks across radiology, pathology, and clinical text, covering classification, detection, segmentation, regression, and vision-language generation. Second, a publicly available evaluation platform that implements, for the first time, a two-step approach to run foundation models for data encoding followed by custom task-specific adaptation via few-shot learning and linear probing mechanisms. Third, we create a meta-model that combines state-of-the-art foundation models in pathology, radiology and language with novel task-specific adapters that address all UNICORN tasks, which we refer to as Unicorn Model-0 (UM-0). Finally, we design a novel UNICORN score to benchmark and compare model performance across all tasks. We present the results of UM-0 using sequestered test data from over 2,400 patients, 3,700 vision cases, and 2,400 clinical reports from 17 institutions across eight countries, spanning eight anatomical regions and four imaging modalities. Data, baselines, and evaluation platform are publicly accessible at unicorn.grand-challenge.org.

Michelle Stegeman, Lena Philipp, Fennie van der Graaf +20

and on behalf of the UNICORN consortium

Computational PathologyMedical Imaging Foundation ModelsHealthcareMultimodal Foundation ModelsMultimodal Model EvaluationMedical Image Benchmarks
AskPDF
Feb 28, 2026

Specializing Foundation Models via Mixture of Low-Rank Experts for Comprehensive Head CT Analysis

Foundation models pre-trained on large-scale datasets demonstrate strong transfer learning capabilities; however, their adaptation to complex multi-label diagnostic tasks-such as comprehensive head CT finding detection-remains understudied. Standard parameter-efficient fine-tuning methods such as LoRA apply uniform adaptations across pathology types, which may limit performance for diverse medical findings. We propose a Mixture of Low-Rank Experts (MoLRE) framework that extends LoRA with multiple specialized low-rank adapters and unsupervised soft routing. This approach enables conditional feature adaptation with less than 0.5% additional parameters and without explicit pathology supervision. We present a comprehensive benchmark of MoLRE across six state-of-the-art medical imaging foundation models spanning 2D and 3D architectures, general-domain, medical-domain, and head CT-specific pretraining, and model sizes ranging from 7M to 431M parameters. Using over 70,000 non-contrast head CT scans with 75 annotated findings-including hemorrhage, infarction, trauma, mass lesions, structural abnormalities, and chronic changes-our experiments demonstrate consistent performance improvements across all models. Gains vary substantially: general-purpose and medical-domain models show the largest improvements (DINOv3-Base: +4.6%; MedGemma: +4.3%), whereas 3D CT-specialized or very large models show more modest gains (+0.2-1.3%). The combination of MoLRE and MedGemma achieves the highest average detection AUC of 0.917. These findings highlight the importance of systematic benchmarking on target clinical tasks, as pretraining domain, architecture, and model scale interact in non-obvious ways.

Youngjin Yoo, Han Liu, Bogdan Georgescu +14

Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ USA · Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany · Department of Radiology, University of Maryland Medical Center, Baltimore, MD USA +4

Medical Imaging Foundation ModelsLow-Rank AdaptationMixture-of-Experts ModelsMedical Image ClassificationComputed TomographyMedical Image Benchmarks
AskPDF
Feb 27, 2026

Extending 2D foundational DINOv3 representations to 3D segmentation of neonatal brain MR images

Precise volumetric delineation of hippocampal structures is essential for quantifying neurodevelopmental trajectories in pre-term and term infants, where subtle morphological variations may carry prognostic significance. While foundation encoders trained on large-scale visual data offer discriminative representations, their 2D formulation is a limitation with respect to the 33D organization of brain anatomy. We propose a volumetric segmentation strategy that reconciles this tension through a structured window-based disassembly-reassembly mechanism: the global MRI volume is decomposed into non-overlapping 3D windows or sub-cubes, each processed via a separate decoding arm built upon frozen high-fidelity features, and subsequently reassembled prior to a ground-truth correspendence using a dense-prediction head. This architecture preserves constant a decoder memory footprint while forcing predictions to lie within an anatomically consistent geometry. Evaluated on the ALBERT dataset for hippocampal segmentation, the proposed approach achieves a Dice score of 0.65 for a single 3D window. The method demonstrates that volumetric anatomical structure could be recovered from frozen 2D foundation representations through structured compositional decoding, and offers a principled and generalizable extension for foundation models for 3D medical applications.

Annayah Usman, Behraj Khan, Tahir Qasim Syed

Institute of Business Administration Karachi

Medical Image Segmentation3D Medical Image SegmentationMedical Imaging Foundation ModelsTransfer Learning
AskPDF
Feb 27, 2026

Topology-Driven Transferability Estimation of Medical Foundation Models for Segmentation

The advent of large-scale self-supervised learning (SSL) has produced a vast zoo of medical foundation models. However, selecting optimal medical foundation models for specific segmentation tasks remains a computational bottleneck. Existing Transferability Estimation (TE) metrics, primarily designed for classification, rely on global statistical assumptions and fail to capture the topological complexity essential for dense prediction. We propose a novel Topology-Driven Transferability Estimation framework that evaluates manifold tractability rather than statistical overlap. Our approach introduces three components: (1) Global Representation Topology Divergence (GRTD), utilizing Minimum Spanning Trees to quantify feature-label structural isomorphism; (2) Local Boundary-Aware Topological Consistency (LBTC), which assesses manifold separability specifically at critical anatomical boundaries; and (3) Task-Adaptive Fusion, which dynamically integrates global and local metrics based on the semantic cardinality of the target task. Validated on the large-scale OpenMind benchmark across diverse anatomical targets and SSL foundation models, our approach significantly outperforms state-of-the-art baselines by around 31% relative improvement in the weighted Kendall metric, providing a robust, training-free proxy for efficient model selection without the cost of fine-tuning. The code will be made publicly available upon acceptance.

Jiaqi Tang, Shaoyang Zhang, Xiaoqi Wang +3

Peking University, Beijing, China · National Institute of Health Data Science, Peking University, Beijing, China · Institute of Medical Technology, Peking University, Beijing, China +3

Task Transferability EstimationMedical Imaging Foundation ModelsMedical Image Segmentation
AskPDF
Feb 26, 2026

Few-Shot Continual Learning for 3D Brain MRI with Frozen Foundation Models

Foundation models pretrained on large-scale 3D medical imaging data face challenges when adapted to multiple downstream tasks under continual learning with limited labeled data. We address few-shot continual learning for 3D brain MRI by combining a frozen pretrained backbone with task-specific Low-Rank Adaptation (LoRA) modules. Tasks arrive sequentially -- tumor segmentation (BraTS) and brain age estimation (IXI) -- with no replay of previous task data. Each task receives a dedicated LoRA adapter; only the adapter and task-specific head are trained while the backbone remains frozen, thereby eliminating catastrophic forgetting by design (BWT=0). In continual learning, sequential full fine-tuning suffers severe forgetting (T1 Dice drops from 0.80 to 0.16 after T2), while sequential linear probing achieves strong T1 (Dice 0.79) but fails on T2 (MAE 1.45). Our LoRA approach achieves the best balanced performance across both tasks: T1 Dice 0.62±\pm0.07, T2 MAE 0.16±\pm0.05, with zero forgetting and <<0.1% trainable parameters per task, though with noted systematic age underestimation in T2 (Wilcoxon p<0.001p<0.001). Frozen foundation models with task-specific LoRA adapters thus offer a practical solution when both tasks must be maintained under few-shot continual learning.

Chi-Sheng Chen, Xinyu Zhang, Guan-Ying Chen +3

Independent Researcher, USA · Indiana University at Bloomington, United States · Independent Researcher, Taiwan +3

Medical Imaging Foundation ModelsBrain MRIBrain Tumor Segmentation
AskPDF
Feb 26, 2026

A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling

Foundation models for medical imaging are typically pretrained on increasingly large datasets, following a "scale-at-all-costs" paradigm. However, this strategy faces two critical challenges: large-scale medical datasets often contain substantial redundancy and severe class imbalance that bias representation learning toward over-represented patterns, and indiscriminate training regardless of heterogeneity in data quality incurs considerable computational inefficiency. Here we demonstrate that active, principled data curation during pretraining can serve as a viable, cost-effective alternative to brute-force dataset enlargement. We introduce CheXficient, a chest X-ray (CXR) foundation model that selectively prioritizes informative training samples. CheXficient is pretrained on only 22.7% of 1,235,004 paired CXR images and reports while consuming under 27.3% of the total compute budget, yet achieving comparable or superior performance to its full-data counterpart and other large-scale pretrained models. We assess CheXficient across 20 individual benchmarks spanning 5 task types, including non-adapted off-the-shelf evaluations (zero-shot findings classification and crossmodal retrieval) and adapted downstream tasks (disease prediction, semantic segmentation, and radiology report generation). Further analyses show that CheXficient systematically prioritizes under-represented training samples, improving generalizability on long-tailed or rare conditions. Overall, our work offers practical insights into the data and computation demands for efficient pretraining and downstream adaptation of medical vision-language foundation models.

Chong Wang, Yabin Zhang, Yunhe Gao +9

Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, CA, USA. · Department of Radiology, Stanford University, Stanford, CA, USA. · Department of Computer Science, Stanford University, Stanford, CA, USA. +3

Medical Imaging Foundation ModelsMedical VLMsData-Efficient LearningData Selection
AskPDF
Feb 24, 2026

OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation

Musculoskeletal disorders represent a significant global health burden and are a leading cause of disability worldwide. While MRI is essential for accurate diagnosis, its interpretation remains exceptionally challenging. Radiologists must identify multiple potential abnormalities within complex anatomical structures across different imaging planes, a process that requires significant expertise and is prone to variability. We developed OrthoDiffusion, a unified diffusion-based foundation model designed for multi-task musculoskeletal MRI interpretation. The framework utilizes three orientation-specific 3D diffusion models, pre-trained in a self-supervised manner on 15,948 unlabeled knee MRI scans, to learn robust anatomical features from sagittal, coronal, and axial views. These view-specific representations are integrated to support diverse clinical tasks, including anatomical segmentation and multi-label diagnosis. Our evaluation demonstrates that OrthoDiffusion achieves excellent performance in the segmentation of 11 knee structures and the detection of 8 knee abnormalities. The model exhibited remarkable robustness across different clinical centers and MRI field strengths, consistently outperforming traditional supervised models. Notably, in settings where labeled data was scarce, OrthoDiffusion maintained high diagnostic precision using only 10% of training labels. Furthermore, the anatomical representations learned from knee imaging proved highly transferable to other joints, achieving strong diagnostic performance across 11 diseases of the ankle and shoulder. These findings suggest that diffusion-based foundation models can serve as a unified platform for multi-disease diagnosis and anatomical segmentation, potentially improving the efficiency and accuracy of musculoskeletal MRI interpretation in real-world clinical workflows.

Tian Lan, Lei Xu, Zimu Yuan +8

Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China. · Department of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine of Peking University, Beijing, China. · Beijing Key Laboratory of Research and Translation for Drugs and Medical Devices in Precision Diagnosis and Treatment of Sports Injuries, Beijing, China. +4

Diffusion Models for Medical Image SegmentationMedical Imaging Foundation ModelsDiffusion ModelsMagnetic Resonance ImagingMedical Image SegmentationMedical Image Classification
AskPDF
Feb 22, 2026

GUIDE-US: Grade-Informed Unpaired Distillation of Encoder Knowledge from Histopathology to Micro-UltraSound

Purpose: Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggressive regions, yet current models struggle to infer tissue micro-structure at coarse imaging resolutions. Methods: We introduce an unpaired histopathology knowledge-distillation strategy that trains a micro-US encoder to emulate the embedding distribution of a pretrained histopathology foundation model, conditioned on International Society of Urological Pathology (ISUP) grades. Training requires no patient-level pairing or image registration, and histopathology inputs are not used at inference. Results: Compared to the current state of the art, our approach increases sensitivity to clinically significant PCa (csPCa) at 60% specificity by 3.5% and improves overall sensitivity at 60% specificity by 1.2%. Conclusion: By enabling earlier and more dependable cancer risk stratification solely from imaging, our method advances clinical feasibility. Source code will be publicly released upon publication.

Emma Willis, Tarek Elghareb, Paul F. R. Wilson +6

Queen’s University, Kingston, Canada. · University of British Columbia, Vancouver, Canada∗. · Exact Imaging, Markham, Canada.

Ultrasound Image ClassificationProstate Cancer GradingMedical Image ClassificationUltrasound ImagingCross-Modal Knowledge DistillationMedical Imaging Foundation Models
AskPDF
Feb 17, 2026

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models

Zero-shot anomaly detection (ZSAD) has gained increasing attention in medical imaging as a way to identify abnormalities without task-specific supervision, but most advances remain limited to 2D datasets. Extending ZSAD to 3D medical images has proven challenging, with existing methods relying on slice-wise features and vision-language models, which fail to capture volumetric structure. In this paper, we introduce a fully training-free framework for ZSAD in 3D brain MRI that constructs localized volumetric tokens by aggregating multi-axis slices processed by 2D foundation models. These 3D patch tokens restore cubic spatial context and integrate directly with distance-based, batch-level anomaly detection pipelines. The framework provides compact 3D representations that are practical to compute on standard GPUs and require no fine-tuning, prompts, or supervision. Our results show that training-free, batch-based ZSAD can be effectively extended from 2D encoders to full 3D MRI volumes, offering a simple and robust approach for volumetric anomaly detection.

Tai Le-Gia, Jaehyun Ahn

Department of Mathematics, Chungnam National University, South Korea

Medical Image Anomaly DetectionZero-Shot Anomaly Detection3D Medical ImagingMedical Imaging Foundation Models
AskPDF
Feb 17, 2026

Foundation Models for Medical Imaging: Status, Challenges, and Directions

Foundation models (FMs) are rapidly reshaping medical imaging, shifting the field from narrowly trained, task-specific networks toward large, general-purpose models that can be adapted across modalities, anatomies, and clinical tasks. In this review, we synthesize the emerging landscape of medical imaging FMs along three major axes: principles of FM design, applications of FMs, and forward-looking challenges and opportunities. Taken together, this review provides a technically grounded, clinically aware, and future-facing roadmap for developing FMs that are not only powerful and versatile but also trustworthy and ready for responsible translation into clinical practice.

Chuang Niu, Pengwei Wu, Bruno De Man +1

Biomedical Imaging Center, Center for Computational Innovations, Center for Biotechnology & Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA · GE HealthCare Technology & Innovation Center, Niskayuna, NY, USA

Medical Imaging Foundation ModelsMedical ImagingMedical Image Analysis
AskPDF
Feb 16, 2026

Scalable next-scale autoregression for medical image generation across anatomical regions

Autoregressive pretraining has been key to the scalability of large language models, yet medical generative foundation models remain predominantly based on diffusion. Here we introduce MedVAR, the first foundation model for all-round medical image generation through autoregressive training, and find it offers improved generation quality and efficiency, stronger scalability, and broader adaptability to downstream clinical tasks than diffusion-based models. A tokenizer trained on medical images and separate semantic and structural controls enable generation across six anatomical regions in computed tomography and magnetic resonance imaging. Trained on 438,905 slices from 40 datasets, including seven internal clinical centres, MedVAR generates images 12-19 times faster than 100-step diffusion baselines. Generation quality improves with model size. Pretraining on generated images improves seven-centre hepatocellular carcinoma segmentation Dice from 0.615 to 0.632, while reconstruction using MedVAR images approaches the volumetric segmentation performance of fully sampled volumes. Membership inference reaches 3.54% sensitivity at a 1% false-positive rate, while copy detection performs near chance. These findings establish next-scale autoregression as a scalable and versatile approach to medical image generation and downstream analysis.

Zhicheng He, Yunpeng Zhao, Junde Wu +7

National University of Singapore · University of Oxford · Zhejiang University +1

Medical Imaging Foundation ModelsHealthcareVisual Autoregressive ModelsAutoregressive Image GenerationMedical Image Synthesis
AskPDF
Feb 12, 2026

Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis

Objective: X-ray angiography is the gold standard imaging modality for cardiovascular diseases. However, current deep learning approaches for X-ray angiogram analysis are severely constrained by the scarcity of annotated data. While large-scale self-supervised learning (SSL) has emerged as a promising solution, its potential in this domain remains largely unexplored, primarily due to the lack of effective SSL frameworks and large-scale datasets. Methods: To bridge this gap, we introduce a vascular anatomy-aware masked image modeling (VasoMIM) framework that explicitly integrates domain-specific anatomical knowledge. Specifically, VasoMIM comprises two key designs: an anatomy-guided masking strategy and an anatomical consistency loss. The former strategically masks vessel-containing patches to compel the model to learn robust vascular semantics, while the latter preserves structural consistency of vessels between the original and reconstructed images, enhancing the discriminability of the learned representations. In conjunction with VasoMIM, we curate XA-170K, the largest X-ray angiogram pre-training dataset to date. Results: We validate VasoMIM on four downstream tasks across six datasets, where it demonstrates superior transferability and achieves state-of-the-art performance compared to existing methods. Conclusion: Incorporating vascular anatomical knowledge into self-supervised pre-training enables more discriminative and transferable representations for X-ray angiogram analysis. Significance: These findings highlight the significant potential of VasoMIM as a foundation model for advancing a wide range of X-ray angiogram analysis tasks. VasoMIM and XA-170K are available on GitHub and Hugging Face, respectively.

De-Xing Huang, Chaohui Yu, Xiao-Hu Zhou +8

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China · DAMO Academy, Alibaba Group, Hangzhou 310023, China +2

Medical Image Representation LearningMedical ImagingSelf-Supervised Pre-TrainingMedical Imaging Foundation ModelsMasked Image Modeling
AskPDF
Jan 1, 2026

TotalFM: An Organ-Separated 3D-CT Foundation Model Leveraging Large-Scale Routine Clinical Radiology Data

While foundation models in radiology are expected to be applied to various clinical tasks, computational cost constraints remain a major challenge when training on 3D-CT volumetric data. In this study, we propose TotalFM, a radiological foundation model that efficiently learns the correspondence between 3D-CT images and linguistic expressions based on the concept of organ separation, utilizing a large-scale dataset of 140,000 series. By automating the creation of organ volume and finding-sentence pairs through segmentation techniques and Large Language Model (LLM)-based radiology report processing, and by combining self-supervised pre-training via VideoMAE with contrastive learning using volume-text pairs, we aimed to balance computational efficiency and representation capability. In zero-shot organ-wise lesion classification tasks, the proposed model achieved higher F1 scores in 83% (5/6) of organs compared to CT-CLIP and 64% (9/14) of organs compared to Merlin. These results suggest that the proposed model exhibits high generalization performance in a clinical evaluation setting using actual radiology report sentences. Furthermore, in zero-shot finding-wise lesion classification tasks, our model achieved a higher AUROC in 83% (25/30) of finding categories compared to Merlin. We also confirmed performance comparable to existing Vision-Language Models (VLMs) in radiology report generation tasks. Our results demonstrate that the organ-separated learning framework can serve as a realistic and effective design guideline for the practical implementation of 3D-CT foundation models. The source code and pretrained models are publicly available at https://github.com/jichi-labo/TotalFM.

Kohei Yamamoto, Tomohiro Kikuchi

Department of Radiology, Jichi Medical University · Data Science Center, Jichi Medical University

3D Foundation ModelsMedical Imaging Foundation ModelsRadiology VLMsMedical Image Classification3D VLMs
AskPDF
Dec 20, 2025

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation

Accurate segmentation of anatomical structures in medical images is essential for diagnosis and treatment planning. While recent interactive segmentation foundation models enhance generalization through large-scale multimodal pretraining, they still depend on precise prompts and can fail in underrepresented clinical contexts (e.g., small organs-at-risk). We present AtlasSegFM, an atlas-guided framework that customizes off-the-shelf foundation models to new clinical contexts with a single annotated example. AtlasSegFM 1) performs atlas-query registration to generate context-aware prompts, 2) refines the segmentation with a frozen foundation model, and 3) applies a lightweight adaptive fusion module to combine atlas priors with foundation-model inputs and predictions. Extensive experiments on six public and in-house datasets across radiotherapy and vascular scenarios show consistent gains, with the largest improvements on small and delicate structures. AtlasSegFM provides a lightweight, deployable solution for one-shot customization of segmentation foundation models in real-world clinical workflows.

Ziyu Zhang, Yi Yu, Simeng Zhu +4

Nanjing University · The Ohio State University · Stanford University

Image SegmentationMedical Imaging Foundation ModelsPromptable Image SegmentationMedical Image Segmentation
AskPDF
Nov 24, 2025

MedSAM3: Delving into Segment Anything with Medical Concepts

Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical application. Here, we propose MedSAM-3, a text promptable medical segmentation model for medical image and video segmentation. By fine-tuning the Segment Anything Model (SAM) 3 architecture on medical images paired with semantic conceptual labels, our MedSAM-3 enables medical Promptable Concept Segmentation (PCS), allowing precise targeting of anatomical structures via open-vocabulary text descriptions rather than solely geometric prompts. We further introduce the MedSAM-3 Agent, a framework that integrates Multimodal Large Language Models (MLLMs) to perform complex reasoning and iterative refinement in an agent-in-the-loop workflow. Comprehensive experiments across diverse medical imaging modalities, including X-ray, MRI, Ultrasound, CT, and video, demonstrate that our approach significantly outperforms existing specialist and foundation models. We will release our code and model at https://github.com/Joey-S-Liu/MedSAM3.

Anglin Liu, Xu R. Cao, Yifan Shen +4

The Hong Kong University of Science and Technology (Guangzhou) · University of Illinois Urbana-Champaign · Southeast University +1

Image SegmentationMedical Imaging Foundation ModelsText-Guided Image SegmentationMedical Image Segmentation
AskPDF
Oct 19, 2025

Foundation Models in Medical Image Analysis: A Systematic Review and Meta-Analysis

Recent advancements in foundation models (FMs) have catalyzed a paradigm shift in medical image analysis. Unlike traditional task-specific artificial intelligence (AI) models, FMs leverage large-scale datasets to learn generalized representations that can be adapted to downstream clinical applications. Despite the rapid proliferation of FM research in medical imaging, there is a lack of unified synthesis that systematically maps the evolution of architectures, training paradigms, and clinical applications across modalities. To address this gap, this review provides a comprehensive and structured synthesis of FMs in medical image analysis by systematically organizing studies into two primary categories: vision-only foundation models (VFMs) and vision-language foundation models (VLFMs), based on their architectural foundations, training strategies, and downstream clinical tasks. A quantitative analysis was conducted on both VFMs and VLFMs to characterize temporal trends in dataset utilization and application domains, along with pooled performance and subgroup analyses. We also critically discuss persistent challenges, including cross-domain generalization, computational scalability, FM evaluation, fairness, and deployment. Finally, we identify key future research directions aimed at enhancing the robustness, interpretability, and clinical integration of FMs, thereby accelerating their translation into real-world medical practice.

Praveenbalaji Rajendran, Mojtaba Safari, Wenfeng He +4

Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322 · Department of Computer Science and Informatics, Emory University, Atlanta, GA 30322

Medical Imaging Foundation ModelsVision Foundation ModelsMedical VLMs
AskPDF
Oct 15, 2025

Scaling Vision Transformers for Functional MRI with Flat Maps

We study the problem of training self-supervised foundation models for functional MRI. Our main contributions are: (1) we introduce a new model family (CortexMAE) trained using the masked autoencoder framework on 2.1K hours of open fMRI data, and (2) we release the first open evaluation suite (Brainmarks) for fMRI foundation models. Our core innovation is simple: we adapt the Vision Transformer to fMRI by first converting each 3D fMRI volume to a 2D map using a cortical flat map projection. We directly compare flat maps to both parcellation and volume-based representations. While each has its advantages, flat maps generally perform best. We perform the first systematic scaling analysis for fMRI and observe strict power law scaling, albeit with limits. Finally, we use Brainmarks to do controlled benchmark comparisons. On subject-level trait prediction, we report a challenging null result: no single model achieves clear state-of-the-art performance. Moreover, all models struggle to outperform a simple functional connectivity baseline. On cognitive state decoding, we observe more robust performance, and in this setting our CortexMAE family outperforms prior models by a large margin. Code, models, and datasets are available at https://github.com/MedARC-AI/CortexMAE and https://github.com/MedARC-AI/Brainmarks.

Connor Lane, Mihir Tripathy, Leema Krishna Murali +15

MedARC · Sophont · Baylor College of Medicine +3

fMRI Foundation ModelsMasked AutoencodersNeuroimagingMedical Image BenchmarksMedical Imaging Foundation ModelsfMRI Representation Learning+1
AskPDF
Oct 8, 2025

Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection

Diabetic Macular Edema (DME) is a leading cause of vision loss among patients with Diabetic Retinopathy (DR). While deep learning has shown promising results for automatically detecting this condition from fundus images, its application remains challenging due the limited availability of annotated data. Foundation Models (FM) have emerged as an alternative solution. However, it is unclear if they can cope with DME detection in particular. In this paper, we systematically compare different FM and standard transfer learning approaches for this task. Specifically, we compare the two most popular FM for retinal images-RETFound and FLAIR-and an EfficientNetB0 backbone, across different training regimes and evaluation settings in IDRiD, MESSIDOR-2 and OCT-and-Eye-FundusImages (OEFI). Results show that despite their scale, FM do not consistently outperform fine-tuned CNNs in this task. In particular, EfficientNet-B0 consistently achieves competitive or superior performance across evaluation settings, with FLAIR being the most competitive foundation model, consistently outperforming RETFound. These findings suggest that FMs do not necessarily provide an advantage for fine-grained ophthalmic tasks such as DME detection, even after fine-tuning, highlighting lightweight CNNs as strong baselines in data-scarce environments.

Franco Javier Arellano, José Ignacio Orlando

Yatiris Group, PLADEMA Institute, UNICEN, Tandil, Argentina · CONICET, Tandil, Argentina

Medical Imaging Foundation ModelsVision Foundation Model AdaptationFundus Imaging
AskPDF
Aug 24, 2025

FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising

Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despite significant advancements driven by deep learning (DL) in recent years, existing DL-based methods, typically trained on a specific dose level and anatomical region, struggle to handle diverse noise characteristics and anatomical heterogeneity during varied scanning conditions, limiting their generalizability and robustness in clinical scenarios. In this paper, we propose FoundDiff, a foundational diffusion model for unified and generalizable LDCT denoising across various dose levels and anatomical regions. FoundDiff employs a two-stage strategy: (i) dose-anatomy perception and (ii) adaptive denoising. First, we develop a dose- and anatomy-aware contrastive language-image pre-training model (DA-CLIP) to achieve robust dose and anatomy perception by leveraging specialized contrastive learning strategies to learn continuous representations that quantify ordinal dose variations and identify salient anatomical regions. Second, we design a dose- and anatomy-aware diffusion model (DA-Diff) to perform adaptive and generalizable denoising by synergistically integrating the learned dose and anatomy embeddings from DA-CLIP into diffusion process via a novel dose and anatomy conditional block (DACB) based on Mamba. Extensive experiments on a large simulated multi-dose CT dataset spanning three anatomical regions, together with cross-dataset evaluations on Mayo-2016, CQ500, and piglet datasets, demonstrate superior denoising performance and strong generalization to unseen dose levels and anatomical regions. The codes and models are available at https: //github.com/hao1635/FoundDiff.

Zhihao Chen, Qi Gao, Zilong Li +4

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

Diffusion Models for Image RestorationMedical Imaging Foundation ModelsComputed TomographyLDCT Denoising
AskPDF
May 27, 2025

Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets

Manually refining radiological segmentation masks is highly resource-intensive. To determine when this expert commitment is truly justified for the training of segmentation models, we investigate the relationship between label quality and model performance. Expanding beyond models trained directly for inference, we conduct the first study isolating the impact of label quality in pre-training datasets. While high-quality labels remain essential for models proceeding directly to deployment, we find no evidence that strict label quality is crucial for pre-training efficacy. These results question the necessity of exhaustive human-in-the-loop refinement for massive corpora intended for pretraining and suggest that expert effort is more effectively invested in well-curated downstream target datasets.

Alexander Jaus, Zdravko Marinov, Constantin Seibold +4

Karlsruhe Institute of Technology, Germany · HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany · Diagnostic and Interventional Radiology, Heidelberg University Hospital +1

Image SegmentationMedical Imaging Foundation ModelsHealthcareMedical Image SegmentationLearning with Noisy LabelsMedical Image Representation Learning
AskPDF
May 6, 2025

Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Tasks

Accurate segmentation of tumors and organs-at-risk is essential for oncology but remains time-consuming and expertise-intensive. Existing AI systems often require manual spatial prompts or task-specific retraining, while generic class labels provide limited guidance for heterogeneous disease targets. Here we present SyRe, a text-prompted segmentation foundation model based on Synergistic vision-language Reinforcement, which strengthens bidirectional interaction between visual and linguistic representations. We introduce Color Region Description (CRD) to generate region-level semantic supervision and construct SyReData, comprising 20 million image-mask-description triplets across 9 modalities and 229 segmentation tasks. Training with diversified prompt formats enables single-target, multi-target and open-ended segmentation, together with invalid-prompt rejection. SyRe achieves accurate segmentation across diverse clinical scenarios, with strong gains on disease-related targets. Evaluation on 28 unseen external datasets, including 17 cancer types, 3 non-cancer diseases and multinational in-house cohorts, demonstrates generalization under distribution shift. Beyond spatial overlap, SyRe-generated masks preserve quantitative pathology information. Across five retrospective tumor imaging cohorts spanning CT and MRI, radiomics derived from these masks stratify survival and improve prognostic modeling over clinical baselines. Clinician-in-the-loop refinement further supports case-level correction. These results establish SyRe as a generalizable framework for scalable quantitative oncology analysis and clinician-guided segmentation.

Haonan Wang, Jiaji Mao, Lehan Wang +11

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

Image SegmentationMedical Imaging Foundation ModelsText-Guided Image SegmentationMedical VLMsMedical Image SegmentationOncology+1
AskPDF
Apr 22, 2025

Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers

Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We developed GlobeReady, a deployment-oriented platform powered by the RetiGlobe foun- dation model and local feature augmentation. RetiGlobe was pretrained in two stages: 1) self-supervised learning using DINOv2 on 38 million synthetic ophthalmic images, and 2) contrastive learning using CLIP on 475,845 real image-text pairs spanning diverse ethnicities, imaging devices, and geographic regions worldwide. We evaluate GlobeReady on 488,448 ophthalmic images, including color fundus photographs (CFPs) and optical coherence tomography scans, from multi-centres in China, Singapore, Vietnam and the UK. Prospective testing included usability assessment with 31 ophthalmologists. Exploratory analyses evaluated domain generalisability, Bayesian uncertainty quantification, out-of-distribution (OOD) detection, and feature-based case retrieval.

Meng Wang, Tian Lin, Qingshan Hou +69

Medical Imaging Foundation ModelsOphthalmic Image AnalysisMedical Image AnalysisMedical VLMsFundus ImagingOphthalmology
AskPDF