Article

Leveraging a Vision-Language Model with Natural Text Supervision for MRI Retrieval, Captioning, Classification, and Visual Question Answering

2025-02-20

Abstract excerpt

Large multimodal models are now extensively used worldwide, with the most powerful ones trained on massive, general-purpose datasets. Despite their rapid deployment, concerns persist regarding the quality and domain relevance of the training data, especially in radiology, medical research, and neuroscience. Additionally, healthcare data privacy is paramount when querying models trained on medical data, as is trans...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 01bbcc3c-ccef-5f43-b1c5-d9ef95d3685a
DOI: 10.1101/2025.02.15.638446

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Leveraging a Vision-Language Model with Natural Text Supervision for MRI Retrieval, Captioning, Classification, and Visual Question AnsweringDOI 10.1101/2025.02.15.638446Read

Health system learning achieves generalist neuroimaging modelsSemantic proximity Read

Merlin: A Vision Language Foundation Model for 3D Computed TomographySemantic proximity Read

Generalizable CT Vision-Language Modeling for Population Health and Disease RiskSemantic proximity Read

DSC-CNN: A Dual-Stream CNN with Cognitive Embedding Fusion for Early Alzheimer’s DiagnosisSemantic proximity Read

A Multimodal Large Language Modelling Deep Learning Framework for the Future PandemicSemantic proximity Read

Forecasting Alzheimer’s Disease Progression with Deep Multimodal Learning: Integration of 3D MRI and Tabular Clinical Records via a Large Vision-Language ModelSemantic proximity Read

A Multimodal Vision-text AI Copilot for Brain Disease Diagnosis and Medical ImagingSemantic proximity Read

CMANet: Cross-Modal Attention Network for 3-D Knee MRI and Report-Guided Osteoarthritis AssessmentSemantic proximity Read

An Empirical Evaluation of Low-Rank Adapted Vision–Language Models for Radiology Medical Image CaptioningSemantic proximity Read

Vision-Language Foundation Models Do Not Transfer to Medical Imaging Classification: A Negative Result on Chest X-ray DiagnosisSemantic proximity Read

CALM-VLM: CALIBRATION AND SELECTIVE PREDICTION IN VISION–LANGUAGE MODELS FOR RELIABLE BRAIN MRI CLASSIFICATIONSemantic proximity Read

Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed TomographySemantic proximity Read