Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks

• URL: http://arxiv.org/abs/2107.13048v1
• Date: Tue, 27 Jul 2021 19:17:37 GMT
• Title: Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks
• Authors: Richard J. Chen, Ming Y. Lu, Muhammad Shaban, Chengkuan Chen, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood
• Abstract summary: We present Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional network that hierarchically aggregates instance-level histology features. We demonstrate that Patch-GCN outperforms all prior weakly-supervised approaches by 3.58-9.46%.
• Score: 6.427108174481534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival. Despite the advancements made in weakly-supervised deep learning, many approaches are not context-aware and are unable to model important morphological feature interactions between cell identities and tissue types that are prognostic for patient survival. In this work, we present Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional network that hierarchically aggregates instance-level histology features to model local- and global-level topological structures in the tumor microenvironment. We validate Patch-GCN with 4,370 gigapixel WSIs across five different cancer types from the Cancer Genome Atlas (TCGA), and demonstrate that Patch-GCN outperforms all prior weakly-supervised approaches by 3.58-9.46%. Our code and corresponding models are publicly available at https://github.com/mahmoodlab/Patch-GCN.

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