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FOMO260K: Brain MRI Dataset for Large-Scale Self-Supervised Learning with Clinical Data

fomo260k

Dataset paper preprint: A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.
https://arxiv.org/pdf/2506.14432.

Description

FOMO260K is a large-scale dataset of brain MRI scans, including both clinical and research-grade scans. The dataset includes a wide range of sequences, including T1, MPRAGE, T2, T2*, FLAIR, SWI, T1c, PD, DWI, ADC, and more.

The dataset consists of

  • 55,378 subjects
  • 77,589 sessions
  • 260,927 scans

The dataset is a superset of both OpenMind and FOMO45K.

dataset_overview

For further details about the dataset, including the preprocessing, please consult the dataset paper preprint.

The FOMO-MRI Dataset Collection

FOMO260K is one of four related datasets in the FOMO-MRI collection. FOMO300K is the full superset; every other dataset in the collection — including this one — is a subset of it. The variants exist to offer different trade-offs between size, access requirements, and preprocessing.

Dataset Scans Access Description
FOMO300K 306,207 Gated (auto-approved) The full superset across the collection. Gated because some constituent datasets require Data Use Agreements.
FOMO260K (this dataset) 260,927 Open (CC-BY-NC-SA) The freely accessible subset of FOMO300K. No login or access request required.
FOMO50K 49,193 Gated (auto-approved) A co-registered, skull-stripped, and/or defaced subset of FOMO300K.
FOMO45K 46,149 Open (CC-BY-NC-SA) The freely accessible subset of FOMO50K. No login or access request required.

⚠️ Do not combine datasets from this collection. Because each dataset is a subset of FOMO300K, combining them will result in duplicated scans.

Comparison to OpenMind and FOMO45K

data_table

FOMO260K encompasses a broader range of disorders and a higher proportion of low-resolution scans, which are typical in clinical practice. In particular, FOMO260K contains considerably more tumors compared to OpenMind. We use slice thickness as a proxy for classifying a scan as "clinical-grade", and define a clinical-grade scan as having a slice thickness above 3mm. We report disorder groups by number of sessions, derived from the disease metadata as described in the dataset paper. Numbers in parentheses indicate the number of sessions with available disease metadata, and percentages are computed with respect to this subset. Note that both OpenMind and FOMO45K are subsets of FOMO260K.

Format

All data is provided as NIfTI-files. The dataset is provided as a collection of datasets, each within its own folder PTXYZ_DatasetName (e.g., PT001_ClevelandCCF). All data follow a slighly modified version of the BIDS format:

-- PT001_ClevelandCCF
   |-- sub-01
       |-- ses-01
           |-- t1w.nii.gz
-- PT002_Nigerian_Clinical
   |-- sub-01
       |-- ses-01
           |-- t1w.nii.gz

Sessions where the sequence information was not available are named scan.nii.gz.

Metadata Files

The dataset includes the following metadata files, available both in the main folder and in each individual dataset folder:

  • participants.tsv: Contains demographic and clinical information including age, gender, handedness, and diagnosis group (e.g., control, specific diagnosis)
  • mapping.tsv: Links the files in FOMO260K to the original data source scans
  • mri_info.tsv: Includes MRI acquisition information

Citation

Users must cite the following paper when using the FOMO260K dataset:

@article{Cerri2026large,
  title={A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning},
  author={Cerri, Stefano and Munk, Asbj{\o}rn and Llambias, Sebastian N{\o}rgaard and Ambsdorf, Jakob and Machnio, Julia and Nersesjan, Vardan and Hedeager Krag, Christian and Liu, Peirong and Rocamora Garc{\'\i}a, Pablo and Mehdipour Ghazi, Mostafa and Boesen, Mikael and Benros, Michael Eriksen and Iglesias, Juan Eugenio and Nielsen, Mads},
  journal={arXiv preprint arXiv:2506.14432},
  year={2026},
  url={https://arxiv.org/abs/2506.14432}
}

In addition, users must comply with all attribution requirements of the constituent datasets included in FOMO260K, as specified in the Usage Notes of the paper.

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