Hierarchical Graph Pathomic Network for Progression Free Survival Prediction
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VIII(2021)
摘要
High resolution histology images contain information related to disease prognosis. However, survival prediction based on current clinical grading systems, which rely heavily on a pathologist's histological assessment, has significant limitations due to the heterogeneity and complexity of tissue phenotypes. To address these challenges, we propose a deep learning framework that leverages hierarchical graph-based representations to enable more precise prediction of progression-free survival in prostate cancer patients. 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 context. Extensive experiments have demonstrated the effectiveness of our model for better progression prediction.
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关键词
Progression free survival, Graph convolutional neural network, Self-supervised learning, Hierarchical graph representations
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