Abstract
Medical foundation models (FMs) are increasingly being used for brain MRI analysis. However, their evaluation remains dominated by high-resource datasets, leaving generalization to African cohorts underexplored. We assess whether FMs generalize equally to African and non-African brain MRI data across two tasks: dementia classification using a Nigerian dataset and brain tumor segmentation using BraTS-Africa. We evaluated two generalist FMs (BrainIAC, 3DINO) and two segmentation-specific FMs (MedSAM2, Medical-SAM2) against a from-scratch baseline. For classification, FMs provide limited gains (highest ROC-AUC of 0.86 with BrainIAC), whereas for segmentation they consistently improve performance, reaching up to 0.86 Dice with MedSAM2. Performance differences between African and non-African cohorts are inconsistent in direction suggesting that FMs do not show a systematic bias against African cohorts. This study also highlights the limited availability and diversity of African neuroimaging datasets as the main barrier to robust evaluation and deployment.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/AFRICAI_011.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=EnzZPszMrM
BibTex
@InProceedings{MouKao_Do_MICCAISAT2026,
author = { Mouheb, Kaouther AND Mosquera Rojas, Gonzalo Esteban AND van Leeuwen, Juancito AND Klein, Stefan AND Bron, Esther E.},
title = { { Do Medical Foundation Models Generalize on the African Brain? } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17264},
month = {pending},
page = {pending}
}