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。

BrainFounder：面向神经影像分析的大脑基础模型

BrainFounder: Towards Brain Foundation Models for Neuroimage Analysis

Transfer learning represents a recent paradigm shift in the way we build
artificial intelligence (AI) systems. In contrast to training task-specific
models, transfer learning involves pre-training deep learning models on a large
corpus of data and minimally fine-tuning them for adaptation to specific tasks.
Even so, for 3D medical imaging tasks, we do not know if it is best to
pre-train models on natural images, medical images, or even synthetically
generated MRI scans or video data. To evaluate these alternatives, here we
benchmarked vision transformers (ViTs) and convolutional neural networks
(CNNs), initialized with varied upstream pre-training approaches. These methods
were then adapted to three unique downstream neuroimaging tasks with a range of
difficulty: Alzheimer's disease (AD) and Parkinson's disease (PD)
classification, "brain age" prediction. Experimental tests led to the following
key observations: 1. Pre-training improved performance across all tasks
including a boost of 7.4% for AD classification and 4.6% for PD classification
for the ViT and 19.1% for PD classification and reduction in brain age
prediction error by 1.26 years for CNNs, 2. Pre-training on large-scale video
or synthetic MRI data boosted performance of ViTs, 3. CNNs were robust in
limited-data settings, and in-domain pretraining enhanced their performances,
4. Pre-training improved generalization to out-of-distribution datasets and
sites. Overall, we benchmarked different vision architectures, revealing the
value of pre-training them with emerging datasets for model initialization. The
resulting pre-trained models can be adapted to a range of downstream
neuroimaging tasks, even when training data for the target task is limited.

通过对多种视觉架构进行基准测试，证明了预训练模型在新兴数据集上的初始化价值，使得这些预训练模型能够适应各种下游神经影像任务，即使目标任务的训练数据有限。