We present FOMO300K, a large-scale, heterogeneous dataset of 318,877 brain Magnetic Resonance Imaging (MRI) scans from 82,678 MRI sessions and 59,969 subjects, aggregated from 920 publicly available sources. The dataset includes both clinical- and research-grade images, multiple MRI sequences, and a wide range of anatomical and pathological variability, including scans with large brain anomalies. Minimal preprocessing was applied to preserve the original image characteristics while reducing entry barriers for new users. Companion code for self-supervised pretraining and finetuning is provided, along with pretrained models. FOMO300K is intended to support the development and benchmarking of self-supervised learning methods in medical imaging at scale.

翻译：我们提出了FOMO300K，这是一个大规模、异质性的数据集，包含来自59,969名受试者、82,678次MRI扫描会话的318,877个脑磁共振成像（MRI）扫描，汇集自920个公开可用的数据源。该数据集同时包含临床级和研究级图像、多种MRI序列，以及广泛的解剖学和病理学变异，包括具有显著脑部异常的扫描。我们仅进行了最少的预处理以保留原始图像特征，同时降低新用户的使用门槛。我们提供了用于自监督预训练和微调的配套代码以及预训练模型。FOMO300K旨在支持大规模医学影像中自监督学习方法的开发与基准测试。