BrainFounder: Towards Brain Foundation Models for Neuroimage Analysis

• URL: http://arxiv.org/abs/2406.10395v1
• Date: Fri, 14 Jun 2024 19:49:45 GMT
• Title: BrainFounder: Towards Brain Foundation Models for Neuroimage Analysis
• Authors: Joseph Cox, Peng Liu, Skylar E. Stolte, Yunchao Yang, Kang Liu, Kyle B. See, Huiwen Ju, Ruogu Fang,
• Abstract summary: This study introduces a novel approach towards the creation of medical foundation models. Our method involves a novel two-stage pretraining approach using vision transformers. BrainFounder demonstrates a significant performance gain, surpassing the achievements of previous winning solutions.
• Score: 6.5388528484686885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to interpret and analyze neurological data. This study introduces a novel approach towards the creation of medical foundation models by integrating a large-scale multi-modal magnetic resonance imaging (MRI) dataset derived from 41,400 participants in its own. Our method involves a novel two-stage pretraining approach using vision transformers. The first stage is dedicated to encoding anatomical structures in generally healthy brains, identifying key features such as shapes and sizes of different brain regions. The second stage concentrates on spatial information, encompassing aspects like location and the relative positioning of brain structures. We rigorously evaluate our model, BrainFounder, using the Brain Tumor Segmentation (BraTS) challenge and Anatomical Tracings of Lesions After Stroke v2.0 (ATLAS v2.0) datasets. BrainFounder demonstrates a significant performance gain, surpassing the achievements of the previous winning solutions using fully supervised learning. Our findings underscore the impact of scaling up both the complexity of the model and the volume of unlabeled training data derived from generally healthy brains, which enhances the accuracy and predictive capabilities of the model in complex neuroimaging tasks with MRI. The implications of this research provide transformative insights and practical applications in healthcare and make substantial steps towards the creation of foundation models for Medical AI. Our pretrained models and training code can be found at https://github.com/lab-smile/GatorBrain.

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