CHIEF

The CHIEF (Clinical Histopathology Imaging Evaluation Foundation) model is a pathology-based model used for cancer diagnosis and prognosis prediction. It extracts pathology imaging features through two complementary pre-training methods, including unsupervised pre-training to identify tile-level features and weakly supervised pre-training to identify patterns across slides. The CHIEF model was developed using 60,530 whole slide images (WSIs) covering 19 different anatomical sites and pre-trained on a 44TB high-resolution pathology imaging dataset to extract microscopic representations useful for cancer cell detection, tumor origin identification, molecular profile characterization, and prognosis prediction. The CHIEF model was validated on 19,491 whole-slide images on 32 independent slide sets from 24 international hospitals and cohorts, with overall performance exceeding state-of-the-art deep learning methods by up to 36.1%, demonstrating its ability to address domain shifts observed in diverse population samples and different slide preparation methods. CHIEF provides a generalizable basis for efficient digital pathology assessment of cancer patients.

The target audience of the CHIEF model is pathologists, cancer researchers and medical data analysis experts. Pathologists can use the CHIEF model for more accurate cancer diagnosis and prognostic assessment, researchers can use it to explore the molecular mechanisms of cancer, and medical data analysis experts can use the model to process and analyze large amounts of pathology data.