FlexiCT provides CT foundation models via agglomerative pretraining on 266227 volumes from 56 datasets that match or exceed task-specific models on five task families while organizing embeddings along tumor-stage gradients.
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Self-supervised pretraining on large unlabeled clinical brain MRI data improves generalization to out-of-domain clinical tasks over supervised in-domain training, with task-specific optimal objectives and limited benefits from model scaling.
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Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
FlexiCT provides CT foundation models via agglomerative pretraining on 266227 volumes from 56 datasets that match or exceed task-specific models on five task families while organizing embeddings along tumor-stage gradients.
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Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Self-supervised pretraining on large unlabeled clinical brain MRI data improves generalization to out-of-domain clinical tasks over supervised in-domain training, with task-specific optimal objectives and limited benefits from model scaling.