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Cornelius Crijnen

cornelius.crijnen@mila.quebec
Doctorat - McGill
Superviseur⋅e principal⋅e
Tal Arbel
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Imagerie médicale
Modèles génératifs
Vision par ordinateur
Site web
Bluesky
GitHub

Publications

Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk
Stefano Cerri
Vardan Nersesjan
Christian Hedeager Krag
Jakob Ambsdorf
Pedro García
Julia Machnio
Peirong Liu
Suhyun Ahn
Nasrin Akbari
Yasmina Al Khalil
Kimberly Amador
Sina Amirrajab
Tal Arbel
Meritxell Bach Cuadra
Ujjwal Baid
Bhakti Baheti
Jaume Banús
Kamil Barbierik
Christoph Brune … (voir 64 de plus)
步岩松
Baptiste Callard
Yuhan Chen
Cornelius Crijnen
Corentin Dancette
Peter Drotár
Prasad Dutande
Nils D. Forkert
Saurabh K. Garg†
Jakub Gazda
Matej Gazda
Benoît Gérin
Partha Ghosh
Weikang Gong
Pedro M. Gordaliza
Sam Hashemi
Tobias Heimann
Fucang Jia
Jiexin Jiang
Emily Kaczmarek
Chris Kang
Seung Kwan Kang
Mohammad Khazaei
Julien Khlaut
Petros Koutsouvelis
Jae Sung Lee
Yuchong Li
Mengye Lyu
Mingchen Ma
Anant Madabhushi
Klaus H. Maier-Hein
Pierre Manceron
Andrés Martínez Mora
Moona Mazher
Felix Meister
Nataliia Molchanova
Steven A. Niederer
Leonard Nürnberg
Jinah Park
Abdul Qayyum
Jonas Richiardi
Antoine Saporta
Branislav Setlak
Ning Shen
Justin Szeto
Constantin Ulrich
Puru Vaish
Vibujithan Vigneshwaran
Leroy Volmer
Zihao Wang
Siqi Wei
Anthony Winder
Jelmer M. Wolterink
Maxence Wynen
Chang YANG
Si Young Yie
Mostafa Mehdipour Ghazi
Akshay Pai
Espen Jimenez‐Solem
Sebastian Nørgaard Llambias
Mikael Boesen
Michael Eriksen Benros
Juan Eugenio Iglesias
Mads Nielsen
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-qualit… (voir plus)y labels are prohibitively costly to obtain. Self-supervised learning (SSL) can address this by leveraging the vast amounts of unlabeled data produced in clinical workflows to train robust \textit{foundation models} that adapt out-of-domain with minimal supervision. However, the development of foundation models for brain MRI has been limited by small pretraining datasets and in-domain benchmarking focused on high-quality, research-grade data. To address this gap, we organized the FOMO25 challenge as a satellite event at MICCAI 2025. FOMO25 provided participants with a large pretraining dataset, FOMO60K, and evaluated models on data sourced directly from clinical workflows in few-shot and out-of-domain settings. Tasks covered infarct classification, meningioma segmentation, and brain age regression, and considered both models trained on FOMO60K (method track) and any data (open track). Nineteen foundation models from sixteen teams were evaluated using a standardized containerized pipeline. Results show that (a) self-supervised pretraining improves generalization on clinical data under domain shift, with the strongest models trained \textit{out-of-domain} surpassing supervised baselines trained \textit{in-domain}. (b) No single pretraining objective benefits all tasks: MAE favors segmentation, hybrid reconstruction-contrastive objectives favor classification, and (c) strong performance was achieved by small pretrained models, and improvements from scaling model size and training duration did not yield reliable benefits.
2026-04-12
arXiv (prépublication)
doi.org
arxiv.org