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.
SonoCLIP presents a mask-guided region-aware vision-language foundation model pretrained on 1.44M fetal ultrasound images, demonstrating superior zero-shot performance.
Donor-driven nodule properties in synthetic CT transfer to real lung CT vision-language tasks while host-driven anatomy properties do not, enabling a label-free diagnostic for model routing.
MetaCLIP-CMR applies CLIP-style contrastive learning to cardiac MRI by treating acquisition metadata as text labels, delivering 86.8% modality and 86.5% view accuracy plus top Dice scores on ACDC/M&Ms segmentation with far less pre-training data than recent large-scale CMR models.
Introduces MMBU benchmark for VLMs in biomedicine and demonstrates that established benchmarks mask perception deficiencies in evaluated models.
EchoPilot delivers state-of-the-art training-free ultrasound video segmentation from a single point prompt by introducing scale-space semantic prompting via S.E.E.D. and reliability-gated memory updates.
EchoVQA is the first large-scale VQA dataset for echocardiography spanning high- and low-quality images across views, with acquisition guidance questions, paired with a low-parameter multimodal prompt model that reports SOTA on several benchmarks.
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).
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.
GRAPE augments prototype medical image classifiers with graph attention for co-occurrence, a mismatch safety check, and open-vocabulary anchoring to support incremental addition of findings from single examples.
AI rewriting tasks that standardize radiology reports erode cross-modal image-text alignment more than they erode clinical entities or hedging language, creating a dissociation termed the slop paradox.
Frozen ViT embeddings in chest radiography suppress small-lesion signal at the CLS token but recover it via patch-local pooling on the same forward pass across multiple models and large cohorts.
OGKD injects inter-class geometry into teacher targets for two distillation losses (GAD on global tokens, LGD on patches) and reports 1.7-2.8% average accuracy gains over prior VLM adaptation methods on 11 medical datasets.
TC-LIA detects mirage in VLMs via layer-wise image patch to question alignment in CLIP encoders, reaching 94.6-94.7% three-class accuracy and under 3% mirage rate across five domains and twelve backbones.
StenCE uses cross-modal contrastive learning on paired ECG-angiography data to learn ECG features that classify severe coronary stenosis, reporting the first high performance on this task.
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.
citing papers explorer
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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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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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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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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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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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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.