CHIEF
Clinical Histopathology Imaging Evaluation Foundation model: trained on 60,530 whole-slide images across 19 anatomical sites and validated on 19,491 slides from 24 hospitals for cancer detection, tumour-origin prediction, genomic profiling and survival.
At a glance
What it does
Slide-level representations built with weak supervision on top of tile features, plus a text embedding of the anatomical site; one model then serves detection, origin, molecular-profile and prognosis heads.
Notable uses in oncology
Cancer detection across 11 cancer types with an area under the curve near 0.94 on external validation in the paper; survival prediction across cancer types.
Tasks, data types and cancers
Architecture
Training data
60,530 WSIs from 19 anatomical sites (public cohorts including TCGA, PAIP and others plus institutional data); validation on 19,491 slides from 24 hospitals worldwide.
Linked datasets
- training TCGA — The Cancer Genome Atlas (via NCI Genomic Data Commons) Free registration
Evaluation
How to run
Weights are distributed from the GitHub repository under non-commercial terms; the pipeline expects CTransPath tile features.
Regulatory status and intended use
Regulatory status is quoted from the source linked above and can change. Research-use-only models must not be used for clinical decisions.
Limitations and bias
- Tile encoder (CTransPath) is weaker than newer ViT-L/H foundation encoders; CHIEF's strength is the slide-level training.
- TCGA is in the training set — external evaluation must avoid it.
Sources
This page is educational — it is not medical advice and does not replace consultation with an oncologist. Diagnostic and treatment decisions are made solely by specialist physicians.