Weakly Supervised

CHIEF (Clinical Histopathology Imaging Evaluation Foundation) is a general-purpose weakly supervised machine learning framework from Harvard Medical School. Trained on 60,530 WSIs spanning 19 anatomical sites (44TB data), CHIEF leverages two complementary pretraining methods: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. Validated on 19,491 WSIs from 32 independent slide sets across 24 hospitals internationally. Outperforms SOTA deep learning methods by up to 36.1%, demonstrating strong generalization across diverse populations and slide preparation methods.

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard AI methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task, often with limited generalizability. To address this challenge, we devised CHIEF, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. Developed using 60,530 whole-slide images spanning 19 anatomical sites, CHIEF outperformed SOTA deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations.