A foundation model for generalized brain MRI analysis
Year: 2024
Type: preprint
Abstract: Artificial intelligence (AI) applied to brain magnetic resonance imaging (MRI) has the potential to improve disease diagnosis and management but requires algorithms with generalizable knowledge that can perform well in a variety of clinical scenarios. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. Foundation models, by leveraging self-supervised learning, pretraining, and targeted adaptation, present a promising paradigm to overcome these limitations. Here, we present Brain Imaging Adaptive Core (BrainIAC), a novel foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adaptation. Trained and validated on 48,519 brain MRIs across a broad spectrum of tasks, we demonstrate that BrainIAC outperforms localized supervised training and other pretrained models, particularly in low-data settings and high-difficulty tasks, allowing for application in scenarios otherwise infeasible. BrainIAC can be integrated into imaging pipelines and multimodal frameworks and may lead to improved biomarker discovery and AI clinical translation.
Source: medRxiv
Authors: Divyanshu Tak, Biniam A. Garomsa, Tafadzwa L. Chaunzwa, Anna Zapaishchykova, Juan Carlos Climent Pardo, Zezhong Ye, John Zielke, Yashwanth Ravipati, Sri Vajapeyam, Maryam Mahootiha, Ceilidh Smith, Ariana M. Familiar, Kevin X. Liu, Sanjay Prabhu, Pratiti Bandopadhayay, Ali Nabavizadeh, Sabine Mueller, Hugo JWL Aerts, Raymond Y. Huang, Tina Y. Poussaint, Benjamin H. Kann
Institutions: Brigham and Women's Hospital, Harvard University, Dana-Farber Cancer Institute, Dana-Farber Brigham Cancer Center, Mass General Brigham, Memorial Sloan Kettering Cancer Center, Boston Children's Hospital, Children's Hospital of Philadelphia, University of Pennsylvania, University of California, San Francisco, Maastricht University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4404946452