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Massive FOMO260K dataset released to boost AI in brain MRI analysis

Researchers have introduced FOMO260K, a substantial dataset comprising over 260,000 3D brain MRI scans. This dataset is designed to facilitate the advancement of self-supervised learning techniques within the field of medical imaging. It incorporates a wide array of image types and anatomical variations, aiming to lower the barrier for developing and evaluating new methods. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Provides a large-scale dataset to accelerate research and development of self-supervised learning models for medical image analysis.

RANK_REASON This is a research paper detailing a new dataset for self-supervised learning in medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

COVERAGE [1]

  1. arXiv cs.CV TIER_1 · Stefano Cerri, Asbj{\o}rn Munk, Sebastian N{\o}rgaard Llambias, Jakob Ambsdorf, Julia Machnio, Vardan Nersesjan, Christian Hedeager Krag, Peirong Liu, Pablo Rocamora Garc\'ia, Mostafa Mehdipour Ghazi, Mikael Boesen, Michael Eriksen Benros, Juan Eugenio Ig ·

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

    arXiv:2506.14432v3 Announce Type: replace-cross Abstract: 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 in…