A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915982389280768 |
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| author | Cerri, Stefano Munk, Asbjørn Llambias, Sebastian Nørgaard Ambsdorf, Jakob Machnio, Julia Nersesjan, Vardan Krag, Christian Hedeager Liu, Peirong García, Pablo Rocamora Ghazi, Mostafa Mehdipour Boesen, Mikael Benros, Michael Eriksen Iglesias, Juan Eugenio Nielsen, Mads |
| author_facet | Cerri, Stefano Munk, Asbjørn Llambias, Sebastian Nørgaard Ambsdorf, Jakob Machnio, Julia Nersesjan, Vardan Krag, Christian Hedeager Liu, Peirong García, Pablo Rocamora Ghazi, Mostafa Mehdipour Boesen, Mikael Benros, Michael Eriksen Iglesias, Juan Eugenio Nielsen, Mads |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning Cerri, Stefano Munk, Asbjørn Llambias, Sebastian Nørgaard Ambsdorf, Jakob Machnio, Julia Nersesjan, Vardan Krag, Christian Hedeager Liu, Peirong García, Pablo Rocamora Ghazi, Mostafa Mehdipour Boesen, Mikael Benros, Michael Eriksen Iglesias, Juan Eugenio Nielsen, Mads Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.14432 |