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A foundation model is a large model trained on a vast amount of data without a specific task in mind, then adapted to many downstream uses.
In artificial intelligence a foundation model is a machine-learning model trained on vast datasets so that it can be applied across a wide range of use cases; large language models are the common example (Wikipedia). In oncology the term covers pathology encoders trained on millions of tiles (UNI, Virchow), single-cell models trained on tens of millions of cells (UCE, GeneCompass), bulk transcriptome models (BulkFormer) and genomic sequence models (Evo 2). A model pretrained on single cells is out of distribution for bulk tumour data, so bulk-native and single-cell-native are different claims.
Showing the technology this term belongs to: Pathology & radiology foundation models.
Shares Self-supervised pretraining (SSL), Fine-tuning versus frozen features, and LoRA, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Transformer and attention, Pathology foundation models: UNI, UNI2, Virchow2, CTransPath, CONCH, TITAN, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Transformer and attention, Single-cell and transcriptome foundation models: UCE, GeneCompass, BulkFormer, BulkRNABert, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Fine-tuning versus frozen features, and LoRA, Transformer and attention, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Pathology foundation models: UNI, UNI2, Virchow2, CTransPath, CONCH, TITAN, Pathology & radiology foundation models, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Self-supervised pretraining (SSL), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Self-supervised pretraining (SSL), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Fine-tuning versus frozen features, and LoRA, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.