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.

翻译：脑健康研究这一蓬勃发展的领域日益借助人工智能（AI）来解读和分析神经学数据。本研究通过整合一个源自41,400名参与者的大规模多模态磁共振成像（MRI）数据集，提出了一种创建医学基础模型的新方法。我们的方法采用一种新颖的两阶段预训练策略，使用视觉Transformer。第一阶段专注于编码一般健康大脑的解剖结构，识别不同脑区的形状和大小等关键特征。第二阶段侧重于空间信息，涵盖脑结构的位置和相对空间关系等方面。我们使用脑肿瘤分割挑战赛（BraTS）数据集和脑卒中后病灶解剖追踪v2.0（ATLAS v2.0）数据集对BrainFounder模型进行了严格评估。BrainFounder表现出显著的性能提升，超越了先前完全监督学习的获胜方案。我们的研究结果强调了同时扩展模型复杂性和源自一般健康大脑的无标注训练数据规模的重要性，这提升了模型在复杂MRI神经影像任务中的准确性和预测能力。这项研究的意义为医疗健康领域提供了变革性的见解和实际应用，并为创建医学AI基础模型迈出了重要步伐。我们的预训练模型和训练代码可在 https://github.com/lab-smile/GatorBrain 获取。