Do Medical Foundation Models Generalize on the African Brain?
arXiv:2607.28771v1 Announce Type: new Abstract: Medical foundation models (FMs) are increasingly 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 evaluate 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 and appear more related to dataset size than data origin. These results suggest that FMs do not exhibit an inherent bias against African cohorts, and highlight the limited availability and diversity of African neuroimaging datasets as the main barrier to robust evaluation and deployment.
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[Submitted on 30 Jul 2026]
Title:Do Medical Foundation Models Generalize on the African Brain?
View a PDF of the paper titled Do Medical Foundation Models Generalize on the African Brain?, by Kaouther Mouheb and Gonzalo Esteban Mosquera Rojas and Juancito van Leeuwen and Stefan Klein and Esther E. Bron
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Abstract:Medical foundation models (FMs) are increasingly 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 evaluate 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 and appear more related to dataset size than data origin. These results suggest that FMs do not exhibit an inherent bias against African cohorts, and highlight the limited availability and diversity of African neuroimaging datasets as the main barrier to robust evaluation and deployment.
Comments: Submitted to the AFRICAI workshop (Held in conjunction with MICCAI 2026, Strasbourg, France)
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.28771 [cs.CV]
(or arXiv:2607.28771v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.28771
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kaouther Mouheb [view email] [v1] Thu, 30 Jul 2026 18:46:57 UTC (190 KB)
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