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Transfer learning reuses what a model learned on one task or dataset to do better on a related task with few labels, the situation for almost every cancer outcome.
Transfer learning re-uses knowledge learned from a task to boost performance on a related task (Wikipedia). Its promise in oncology is the low-label regime: outcome labels number in the hundreds, so a model pretrained on unlabelled data should need fewer of them. The gain is largest with fifty to a few hundred labels and tends to vanish as labels grow, and it can be washed out entirely by widening the feature set, so claims should state the label count.
Shares Zero-shot prediction, Fine-tuning versus frozen features, and LoRA, 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.
Shares Self-supervised pretraining (SSL), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Self-supervised pretraining (SSL), Fine-tuning versus frozen features, and LoRA, 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.
Shares Zero-shot prediction, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Zero-shot prediction, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.