sofieneb/uni2h_features

UNI2-h Patch Features for TCGA and CPTAC Pre-extracted patch-level embeddings from the UNI2-h pathology foundation model for 11,759 whole-slide images (WSIs): 9,838 from TCGA (32 projects) and 1,921 from CPTAC (9 cohorts). Features were extracted with TRIDENT. They are meant for weakly supervised slide-level tasks, such as multiple-instance learning (MIL) for classification, survival or biomarker prediction, without having to download or process the raw WSIs. These features were… See the full description on the dataset page: https://huggingface.co/datasets/sofieneb/uni2h_features.

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