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<p>We present FOMO260K, a large-scale, heterogeneous dataset of 260,927 brain Magnetic Resonance Imaging (MRI) scans from 77,589 MRI sessions and 55,378 subjects, aggregated from 910 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. FOMO260K is intended to support the development and benchmarking of self-supervised learning methods in medical imaging at scale.</p>

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A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

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  • Stefano Cerri,
  • Asbjørn Munk,
  • Sebastian Nørgaard Llambias,
  • Jakob Ambsdorf,
  • Julia Machnio,
  • Vardan Nersesjan,
  • Christian Hedeager Krag,
  • Peirong Liu,
  • Pablo Rocamora García,
  • Mostafa Mehdipour Ghazi,
  • Mikael Boesen,
  • Michael Eriksen Benros,
  • Juan Eugenio Iglesias,
  • Mads Nielsen

摘要

We present FOMO260K, a large-scale, heterogeneous dataset of 260,927 brain Magnetic Resonance Imaging (MRI) scans from 77,589 MRI sessions and 55,378 subjects, aggregated from 910 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. FOMO260K is intended to support the development and benchmarking of self-supervised learning methods in medical imaging at scale.