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A pathology foundation model for cancer diagnosis and prognosis prediction
A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.
Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. Researchers may obtain de-identified data directly from DFCI, BWH, YH, SMCH, CUCH and the Hospital of the University of Pennsylvania by reasonable request and subject to institutional ethical approvals. Here, we showed that CHIEF successfully predicted IDH mutation status in both high and low histological grade groups defined by conventional visual-based histopathology assessment.
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