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 this https URL

通过整合来自 41400 名参与者的 MRI 数据集，利用视觉转换器进行两阶段预训练方法，该研究引入了一种新颖的方法来创建医学基础模型，并通过 MRI 在复杂的神经影像任务中提高了模型的准确性和预测能力。