NeuroQA is a large-scale 3D brain MRI visual question answering benchmark with verified image-grounded QA pairs, multi-domain coverage, and baseline evaluations showing current models lag behind text-only performance.
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BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
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abstract
Biomedical data is inherently multimodal, comprising physical measurements and natural language narratives. A generalist biomedical AI model needs to simultaneously process different modalities of data, including text and images. Therefore, training an effective generalist biomedical model requires high-quality multimodal data, such as parallel image-text pairs. Here, we present PMC-15M, a novel dataset that is two orders of magnitude larger than existing biomedical multimodal datasets such as MIMIC-CXR, and spans a diverse range of biomedical image types. PMC-15M contains 15 million biomedical image-text pairs collected from 4.4 million scientific articles. Based on PMC-15M, we have pretrained BiomedCLIP, a multimodal foundation model, with domain-specific adaptations tailored to biomedical vision-language processing. We conducted extensive experiments and ablation studies on standard biomedical imaging tasks from retrieval to classification to visual question-answering (VQA). BiomedCLIP achieved new state-of-the-art results in a wide range of standard datasets, substantially outperforming prior approaches. Intriguingly, by large-scale pretraining on diverse biomedical image types, BiomedCLIP even outperforms state-of-the-art radiology-specific models such as BioViL in radiology-specific tasks such as RSNA pneumonia detection. In summary, BiomedCLIP is a fully open-access foundation model that achieves state-of-the-art performance on various biomedical tasks, paving the way for transformative multimodal biomedical discovery and applications. We release our models at https://aka.ms/biomedclip to facilitate future research in multimodal biomedical AI.
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representative citing papers
CheXTemporal supplies paired chest X-rays with explicit temporal progression taxonomy and spatial grounding to benchmark and improve models on longitudinal reasoning tasks.
HalluCXR benchmark shows 61.9-82.3% hallucination rates across VLMs on MIMIC-CXR images, identifies patterns such as length-based risk and over-fabrication of common findings, and demonstrates ensemble mitigation that cuts fabrication by up to 84.8%.
MedCRP-CL discovers semantic modalities online via CRP from text prompts and maintains modality-specific LoRA adapters with intra-modality EWC, achieving 73.3% Dice and 4.1% forgetting on 16 tasks while using 6x fewer parameters than the best baseline.
Next-acceleration-scale autoregressive prediction in discrete latent space with on-policy privileged information distillation yields improved MRI reconstructions from sparse measurements on the fastMRI benchmark.
Medical VLMs frequently select negated options that contradict visible chest X-ray findings, achieving only ~30% accuracy on direct presence probes, but a post-hoc consistency verifier raises accuracy above 95%.
iTRIALSPACE generates realistic virtual lesion trials on lung CTs that isolate performance drivers and show strong transfer of model rankings to real clinical data (ρ=0.93).
MedLayBench-V is the first large-scale multimodal benchmark for expert-lay semantic alignment in medical vision-language models, constructed via a Structured Concept-Grounded Refinement pipeline that uses UMLS CUIs to enforce equivalence.
CoDA chains clinically plausible acquisition, reconstruction, display, and delivery shifts to substantially degrade zero-shot performance of medical vision-language models, with a post-hoc token-space repair partially recovering accuracy.
CardioBench is a new public benchmark that standardizes eight echocardiography datasets into four regression and five classification tasks to evaluate foundation model generalization.
Large-scale benchmark of noisy-label methods on frozen VFMs reveals no universal winner, with ELR and CUFIT performing differently, and demonstrates small-loss assumption failure via 53-61% loss overlap under asymmetric noise.
Contrastive pretraining on mammography atlas image-text pairs improves BI-RADS classification F1 by 1-14% especially in low-label regimes, outperforming equivalent numbers of direct labels in some settings.
BTECF encodes retinal vessels as Bézier trees to enable targeted, parameter-level counterfactual interventions on vessel geometry for causal analysis of vascular diseases.
CLEF, a long-context EEG foundation model using 3D multitaper spectrograms and contrastive alignment with reports and EHR, beats prior models on 229 of 234 clinical tasks and raises mean AUROC from 0.65 to 0.74.
MSD-Score introduces multi-scale distributional scoring on von Mises-Fisher mixtures to evaluate image captions without references and reports state-of-the-art correlation with human judgments.
CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
Natural-domain foundation models provide competitive and more robust priors than task-specific models for accelerated cardiac MRI reconstruction in cross-domain settings.
REVEAL uses vision-language alignment of retinal morphometry and clinical risk narratives plus group contrastive learning to predict AD and dementia about 8 years early.
BETA adapts black-box models at test time using a local steering model and regularization techniques to achieve accuracy improvements without additional API queries or high latency.
A trajectory-aware process reward using DTW on sentence embeddings, combined with exact-match in GRPO after SFT, raises mean medical VQA accuracy from 0.598 to 0.689 across six benchmarks.
LLaBIT is a single instruction-finetuned LLM that performs report generation, VQA, segmentation, and translation on brain MRI images while outperforming task-specific models.
A VLM framework with spatial patch cross-attention and adaptive PID-Tversky loss reports 90.69% classification accuracy, 0.9512 Dice score, and 92.80 CIDEr for LSS diagnosis plus automated report generation.
A video-trained large vision model achieves competitive zero-shot performance on organ segmentation, denoising, super-resolution, and 4D CT motion prediction in medical imaging, outperforming some specialized baselines on patient data from 122 cases.
VA-Adapter adapts ultrasound foundation models for echocardiography probe guidance by embedding a vision-action module that infers individual 3D cardiac anatomy from historical sequences, outperforming prior methods with roughly 33 times fewer trainable parameters on a 1.31 million sample dataset.
citing papers explorer
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NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding
NeuroQA is a large-scale 3D brain MRI visual question answering benchmark with verified image-grounded QA pairs, multi-domain coverage, and baseline evaluations showing current models lag behind text-only performance.
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CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography
CheXTemporal supplies paired chest X-rays with explicit temporal progression taxonomy and spatial grounding to benchmark and improve models on longitudinal reasoning tasks.
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HalluCXR: Benchmarking and Mitigating Hallucinations in Medical Vision-Language Models for Chest Radiograph Interpretation
HalluCXR benchmark shows 61.9-82.3% hallucination rates across VLMs on MIMIC-CXR images, identifies patterns such as length-based risk and over-fabrication of common findings, and demonstrates ensemble mitigation that cuts fabrication by up to 84.8%.
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MedCRP-CL: Continual Medical Image Segmentation via Bayesian Nonparametric Semantic Modality Discovery
MedCRP-CL discovers semantic modalities online via CRP from text prompts and maintains modality-specific LoRA adapters with intra-modality EWC, achieving 73.3% Dice and 4.1% forgetting on 16 tasks while using 6x fewer parameters than the best baseline.
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Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction
Next-acceleration-scale autoregressive prediction in discrete latent space with on-policy privileged information distillation yields improved MRI reconstructions from sparse measurements on the fastMRI benchmark.
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CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs
Medical VLMs frequently select negated options that contradict visible chest X-ray findings, achieving only ~30% accuracy on direct presence probes, but a post-hoc consistency verifier raises accuracy above 95%.
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iTRIALSPACE: Programmable Virtual Lesion Trials for Controlled Evaluation of Lung CT Models
iTRIALSPACE generates realistic virtual lesion trials on lung CTs that isolate performance drivers and show strong transfer of model rankings to real clinical data (ρ=0.93).
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MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models
MedLayBench-V is the first large-scale multimodal benchmark for expert-lay semantic alignment in medical vision-language models, constructed via a Structured Concept-Grounded Refinement pipeline that uses UMLS CUIs to enforce equivalence.
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CoDA: Exploring Chain-of-Distribution Attacks and Post-Hoc Token-Space Repair for Medical Vision-Language Models
CoDA chains clinically plausible acquisition, reconstruction, display, and delivery shifts to substantially degrade zero-shot performance of medical vision-language models, with a post-hoc token-space repair partially recovering accuracy.
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CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?
CardioBench is a new public benchmark that standardizes eight echocardiography datasets into four regression and five classification tasks to evaluate foundation model generalization.
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Rethinking Noise-Robust Training for Frozen Vision Foundation Models: A Cross-Dataset Benchmark with a Case Study of Small-Loss Failure
Large-scale benchmark of noisy-label methods on frozen VFMs reveals no universal winner, with ELR and CUFIT performing differently, and demonstrates small-loss assumption failure via 53-61% loss overlap under asymmetric noise.
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MAM-CLIP: Vision-Language Pretraining on Mammography Atlases for BI-RADS Classification
Contrastive pretraining on mammography atlas image-text pairs improves BI-RADS classification F1 by 1-14% especially in low-label regimes, outperforming equivalent numbers of direct labels in some settings.
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A General B\'ezier Tree Encoding Counterfactual Framework for Retinal-Vessel-Mediated Disease Analysis
BTECF encodes retinal vessels as Bézier trees to enable targeted, parameter-level counterfactual interventions on vessel geometry for causal analysis of vascular diseases.
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CLEF: EEG Foundation Model for Learning Clinical Semantics
CLEF, a long-context EEG foundation model using 3D multitaper spectrograms and contrastive alignment with reports and EHR, beats prior models on 229 of 234 clinical tasks and raises mean AUROC from 0.65 to 0.74.
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MSD-Score: Multi-Scale Distributional Scoring for Reference-Free Image Caption Evaluation
MSD-Score introduces multi-scale distributional scoring on von Mises-Fisher mixtures to evaluate image captions without references and reports state-of-the-art correlation with human judgments.
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CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
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Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?
Natural-domain foundation models provide competitive and more robust priors than task-specific models for accelerated cardiac MRI reconstruction in cross-domain settings.
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REVEAL: Multimodal Vision-Language Alignment of Retinal Morphometry and Clinical Risks for Incident AD and Dementia Prediction
REVEAL uses vision-language alignment of retinal morphometry and clinical risk narratives plus group contrastive learning to predict AD and dementia about 8 years early.
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Adapting in the Dark: Efficient and Stable Test-Time Adaptation for Black-Box Models
BETA adapts black-box models at test time using a local steering model and regularization techniques to achieve accuracy improvements without additional API queries or high latency.
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Improving Medical VQA through Trajectory-Aware Process Supervision
A trajectory-aware process reward using DTW on sentence embeddings, combined with exact-match in GRPO after SFT, raises mean medical VQA accuracy from 0.598 to 0.689 across six benchmarks.
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Visual Instruction-Finetuned Language Model for Versatile Brain MR Image Tasks
LLaBIT is a single instruction-finetuned LLM that performs report generation, VQA, segmentation, and translation on brain MRI images while outperforming task-specific models.
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An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis
A VLM framework with spatial patch cross-attention and adaptive PID-Tversky loss reports 90.69% classification accuracy, 0.9512 Dice score, and 92.80 CIDEr for LSS diagnosis plus automated report generation.
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Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?
A video-trained large vision model achieves competitive zero-shot performance on organ segmentation, denoising, super-resolution, and 4D CT motion prediction in medical imaging, outperforming some specialized baselines on patient data from 122 cases.
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VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidance
VA-Adapter adapts ultrasound foundation models for echocardiography probe guidance by embedding a vision-action module that infers individual 3D cardiac anatomy from historical sequences, outperforming prior methods with roughly 33 times fewer trainable parameters on a 1.31 million sample dataset.
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RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction
RA-RRG extracts key phrases with LLMs, retrieves them via multimodal similarity, and conditions report generation on them to achieve SOTA CheXbert scores and competitive RadGraph F1 on MIMIC-CXR and IU X-ray while supporting multi-view inputs.
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LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
LLaVA-Med is created via curriculum fine-tuning on PubMed figure-caption pairs and GPT-4 self-instructed data, achieving competitive or better results than prior supervised models on three biomedical VQA benchmarks.
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PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
PMC-VQA dataset and MedVInT model achieve better generative performance on medical VQA benchmarks by visual instruction tuning on a newly constructed large-scale dataset.
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Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
FlexiCT provides CT foundation models via agglomerative pretraining on 266227 volumes from 56 datasets that match or exceed task-specific models on five task families while organizing embeddings along tumor-stage gradients.
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A Human-in-the-Loop Framework for Efficient Prompt Selection in Microscopy Vision-Language Models
A target-driven active learning approach for building efficient prompt sets in microscopy VLMs reaches 100% test accuracy with an average of 20 expert-verified images, outperforming random selection.
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Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosis
Rad-VLSM is a cross-modal two-stage framework that converts semantic guidance from BLIP-2 into box prompts for SAM-based lesion segmentation and then uses the resulting masks as spatial priors in a visual-radiomics fusion head for diagnosis.
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MedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis
MedMIX combines intra-modality expert fusion, learned inter-modality fusion, and training-only large-small collaboration to deliver robust multimodal medical prediction under incomplete modalities across three benchmarks.
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BiomedAP: A Vision-Informed Dual-Anchor Framework with Gated Cross-Modal Fusion for Robust Medical Vision-Language Adaptation
BiomedAP improves robustness of biomedical VLMs to prompt variations using gated cross-modal fusion and dual-anchor constraints, outperforming baselines on 11 benchmarks.
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Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study
DiffKT3D transfers priors from video diffusion models to 3D radiotherapy dose prediction via modality-specific embeddings and clinically guided RL, reducing voxel MAE from 2.07 to 1.93 and claiming SOTA over the GDP-HMM challenge winner.
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Cross-Modal Semantic-Enhanced Diffusion Framework for Diabetic Retinopathy Grading
CGSD framework reaches 87.5% accuracy and 0.731 macro F1 on APTOS 2019 by conditioning diffusion denoising on dot-product vectors from image features and DR-grade text descriptions.
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MultiMedVision: Multi-Modal Medical Vision Framework
A unified Sparse Vision Transformer learns joint 2D/3D medical image representations via self-supervision and achieves competitive AUROC on chest X-ray and CT benchmarks with 5x less data than modality-specific models.
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CapCLIP: A Vision-Language Representation Alignment Approach for Wireless Capsule Endoscopy Analysis
CapCLIP uses pathology-aware text captions to align WCE images in a vision-language space, outperforming standard models in zero-shot classification and retrieval on unseen data.
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Pan-FM: A Pan-Organ Foundation Model with Saliency-Guided Masking for Missing Robustness
Pan-FM learns balanced representations across seven organs by adaptively masking dominant organs during pre-training, yielding stronger disease prediction and missing-organ robustness than single-organ or naive multimodal baselines on UK Biobank.
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Learning from Medical Entity Trees: An Entity-Centric Medical Data Engineering Framework for MLLMs
A Medical Entity Tree organizes medical knowledge to engineer higher-quality training data that boosts general MLLMs on medical benchmarks.
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Align then Refine: Text-Guided 3D Prostate Lesion Segmentation
A text-guided multi-encoder U-Net with alignment loss, heatmap calibration, and confidence-gated cross-attention refiner sets new state-of-the-art 3D prostate lesion segmentation performance on the PI-CAI dataset.
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T-Gated Adapter: A Lightweight Temporal Adapter for Vision-Language Medical Segmentation
A temporal adapter injects adjacent-slice context into VLM token representations, raising mean Dice from 0.498 to 0.704 on FLARE22 and reducing cross-domain drop from 38% to 24.9%.
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A Utility-preserving De-identification Pipeline for Cross-hospital Radiology Data Sharing
The UPDP pipeline filters privacy terms and generates de-identified radiology images that preserve diagnostic pathology information, enabling models with competitive disease detection accuracy but reduced identity leakage and improved cross-hospital performance.
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Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
Introduces Hybrid Tuning adapter with frequency filtering and noise estimation to adapt CLIP for ultrasound segmentation and classification, claiming outperformance on six multi-center datasets.
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Enhancing the Safety of Medical Vision-Language Models by Synthetic Demonstrations
Synthetic clinical demonstrations at inference time improve safety of Med-VLMs against visual and textual jailbreaks while preserving general performance on medical tasks.
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Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning
Lingshu is a medical-specialized multimodal LLM that outperforms prior open-source models on multimodal QA, text QA, and report generation after training on a large curated dataset of medical knowledge.
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CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation
CoRE aligns image tokens to a hierarchical concept library to simulate clinical reasoning for expert routing and demand-based growth in continual brain lesion segmentation, achieving SOTA on 12 tasks.
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Structure-Augmented Standard Plane Detection with Temporal Aggregation in Blind-Sweep Fetal Ultrasound
Structure augmentation via segmentation prior plus temporal aggregation stabilizes keyframe detection of fetal abdomen planes in blind-sweep ultrasound.
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Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
A graph autoencoder model using foundation model features achieves high retrieval accuracy (mAP 96.7-97.6%, mMV 91.5-94.2%) on BreakHis and BACH breast cancer histopathology datasets.