Hierarchical Graph Pathomic Network for Progression Free Survival Prediction
Authors: Zichen Wang, Jiayun Li, Zhufeng Pan, Wenyuan Li, Anthony Sisk, Huihui Ye, William Speier, and Corey W. Arnold
Introduction
This repository is the Pytorch implementation of our MICCAI 2021 paper 'Hierarchical graph pathomic network for progression free survival prediction'.
Hierarchical graph pathomic network is a deep learning framework that leverages hierarchical graph-based representations to enable more precise prediction of progression-free survival. Unlike conventional approaches that analyze patch-based or cell-based pathomic features alone without considering their spatial connectivity, we explore multi-scale topological structures of whole slide images in an integrative contex.
Questions
Please send any questions you might have about this repository to zcwang0702@ucla.edu