Patch-level UNI feature embeddings from colorectal cancer whole slide images (WSIs) in the SurGen dataset

This dataset contains UNI patch embeddings derived from the SurGen cohort's whole slide images (WSIs), focused on colorectal cancer cases. Each WSI was processed into 224x224 pixel tissue patches, extracted at a scale of 1.0 microns per pixel (MPP). A 1024-dimensional embedding was computed for each patch using the UNI foundation model[1]. This dataset allows for rapid downstream analysis of tasks such as biomarker prediction, survival analysis, tumour grading, and prognostic modelling. The SurGen dataset, comprising both primary colorectal and metastatic cases, offers a valuable resource for computational pathology research. Each Zarr file within the dataset contains an array of patch-level features and a corresponding array of coordinates, enabling the retrieval of specific feature locations as needed. Embeddings are provided in a zip archive and intended for reuse in research focused on digital pathology, tumour genomics, and oncology. For cohort ground truth labels please see the link below. Access the original dataset: https://doi.org/10.6019/S-BIAD1285 GitHub for more info: https://github.com/CraigMyles/SurGen-Dataset [1] Chen, R.J., Ding, T., Lu, M.Y., Williamson, D.F.K., et al. Towards a general-purpose foundation model for computational pathology. Nat Med (2024). https://doi.org/10.1038/s41591-024-02857-3