Loading
Contrastive learning trains a model to pull matching pairs (two views of one slide, or one patient's RNA and protein) close together in embedding space and push non-matching pairs apart.
Contrastive methods are a branch of self-supervised learning in which the model learns by comparing positive pairs against negatives drawn from the batch (Wikipedia on self-supervised learning); the InfoNCE loss is the usual objective. It powers vision-language pathology models that align tile images with report text (CONCH) and multimodal patient models that align a patient's modalities. Its weakness is that it needs many negatives and can learn the batch rather than the biology.
Shares Multimodal fusion (early, late, modality dropout), Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Multimodal fusion (early, late, modality dropout), Embedding (learned representation), 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 Multimodal fusion (early, late, modality dropout), 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 Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.