Self-supervised Text-vision Alignment for Automated Brain MRI Abnormality Detection: A Multicenter Study (ALIGN Study).
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Authors
Wood, David A
Guilhem, Emily
Kafiabadi, Sina
Al Busaidi, Ayisha
Dissanayake, Kishan
Hammam, Ahmed
Mansoor, Nina
Townend, Matthew
Agarwal, Siddharth
Wei, Yiran
Issue Date
2025-11-26
Type
Journal Article
Language
en
Keywords
Alternative Title
Abstract
Purpose To develop a self-supervised text-vision framework to detect abnormalities on brain MRI scans by leveraging free-text neuroradiology reports, eliminating the need for expertlabeled training datasets. Materials and Methods This retrospective and prospective multicenter study included 81,936 brain MRI examinations and corresponding radiology reports for adult patients at two UK National Health Service (NHS) hospitals during January 2008-December 2019 for training and internal testing, and 1,369 prospectively collected examinations between March 2022-March 2024 from four separate NHS hospitals for external testing (clinicaltrials.gov NCT043681). A neuroradiology language model (NeuroBERT) was trained using self-supervised tasks to generate report embeddings. Convolutional neural networks (one per MRI sequence) were trained to map scans to embeddings by minimizing mean squared error loss. The framework then detected abnormalities in new examinations by scoring scans against query sentences using textimage similarity. Model diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). Results The framework achieved an AUC of 0.95 (95% CI: 0.94, 0.97) for normal versus abnormal classification and generalized to external sites with examination-level AUCs of 0.90 (95% CI: 0.86, 0.93), 0.87 (95% CI: 0.83, 0.90), 0.86 (95% CI: 0.83, 0.90), and 0.85 (95% CI: 0.81, 0.89). In five zero-shot classification tasks-acute stroke, multiple sclerosis, intracranial hemorrhage, meningioma, and hydrocephalus-the framework achieved a mean AUC of 0.89 (range, 0.77-0.93). For visual-semantic image retrieval, mean precision was 0.84 among the top 15 images across seven pathologies. Conclusion The self-supervised text-vision framework accurately detected brain MRI abnormalities without expert-labeled datasets. © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license.
Description
Citation
Wood, DA. et al. (2025) 'Self-supervised Text-vision Alignment for Automated Brain MRI Abnormality Detection: A Multicenter Study (ALIGN Study)', Radiology: Artificial Intelligence. Available At: https://doi.org/10.1148/ryai.240619
Publisher
Radiological Society of North America
License
Journal
Radiology. Artificial intelligence
Volume
Issue
PubMed ID
ISSN
2638-6100