Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Clinical Histopathology Imaging Evaluation Foundation Model
| Date | Stars |
|---|---|
| 2026-07-31 | 715 |
| 2026-08-06 | 716 |
Today
+1 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# CHIEF - Clinical Histopathology Imaging Evaluation Foundation Model <a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a> ### A Pathology Foundation Model for Cancer Diagnosis and Prognosis Prediction Wang X^, Zhao J^, Marostica E, Yuan W, Jin J, Zhang J, Li R, Tang H, Wang K, Li Y, Wang F, Peng Y, Zhu J, Zhang J, Jackson CR, Zhang J, Dillon D, Lin NU, Sholl L, Denize T, Meredith D, Ligon KL, Signoretti S, Ogino S, Golden JA, Nasrallah MP, Han X, Yang S<sup>+</sup>, Yu KH<sup>+</sup>. Nature (2024). https://www.nature.com/articles/s41586-024-07894-z *Lead Contact: Kun-Hsing Yu, M.D., Ph.D.* #### ABSTRACT *Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence (AI) methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations. To address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, 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. We developed CHIEF using 60,530 whole-slide mimages (WSIs) spanning 19 distinct anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumor origin identification, molecular profile characterization, and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for cancer patients.*  ## 👑👑👑 Encoding one WSI as one feature representation. Many downstream clinical applications (e.g., survival analysis, drug discovery, and the identification of unknown subtypes via unsupervised clustering), rely on encoding a single feature that effectively represents an entire slide. Therefore, in addition to patch-level (region-of-interest) encoding, CHIEF also focuses on whole slide image (WSI)-level embedding without fine-tuning the tile aggregator. Docker images (model weights) are available at https://hub.docker.com/r/chiefcontainer/chief/ ### ⚡️⚡️⚡️ Applications Empowered by CHIEF. We support a collaborative community effort to implement CHIEF across various applications, including but not limited to digital pathology. Below is a continuously updated list of published works that have utilized CHIEF in different contexts with a particular emphasis on the WSI-level feature we highlighted. * Generablizablity for Cryosection Pathology (biopsy and intraoperative frozen section examination) >Yifan Yuan et al, **AI-augmented intraoperative decision-making workflows in diffuse midline glioma biopsy using cryosection pathology** (2025) _Nature Communications_ (2025; Editors' Highlights) https://www.nature.com/articles/s41467-025-66853-y * Efficient Pathology Image Analysis >Peter Neidlinger et al, **A deep learning framework for efficient pathology image analysis** (2025)
Excerpt of 10,243 characters
Read on GitHub
23
21
3
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b9ec90751ff62606, desc:clinical