mlsanityData to 7 Oct 2026

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

arXiv 2506.14432 ↗First posted 17 Jun 2025eess.IV cs.CVcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 20 of its 786 references that are in the map. First posted on arXiv 17 Jun 2025.

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20 of 786 references.

References · 20

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8 more
  1. 2023The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRIcited by 35
  2. 2019Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRIcited by 30
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2026Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challengecited by 2
  2. 2026From 100,000+ images to winning the first brain MRI foundation model challenges: Sharing lessons and modelscited by 1
  3. 2025A Foundation Model for Brain MRI with Dynamic Modality Integrationcited by 0
  4. 2025Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentationcited by 0
  5. 2025Lost in Distortion: Uncovering the Domain Gap Between Computer Vision and Brain Imaging -- A Study on Pretraining for Age Predictioncited by 0
  6. 2026How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Modelscited by 0
  7. 2026Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Priorcited by 0