Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obtain. The FOMO25 challenge, organized at MICCAI 2025, provided researchers with the FOMO60K pretraining dataset and evaluated foundation models on clinical workflow data across three tasks: infarct classification, meningioma segmentation, and brain age regression, comparing nineteen models from sixteen teams. Self-supervised pretraining improved performance on clinical data experiencing domain shift. Different pretraining approaches suited different tasks, masked autoencoders for segmentation and hybrid objectives for classification, and smaller pretrained models achieved strong results while scaling provided unreliable benefits.
Abstract
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obtain. The FOMO25 challenge, organized at MICCAI 2025, provided researchers with the FOMO60K pretraining dataset and evaluated foundation models on clinical workflow data across three tasks: infarct classification, meningioma segmentation, and brain age regression, comparing nineteen models from sixteen teams.
Self-supervised pretraining improved performance on clinical data experiencing domain shift. Different pretraining approaches suited different tasks, masked autoencoders for segmentation and hybrid objectives for classification, and smaller pretrained models achieved strong results while scaling provided unreliable benefits.