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Adaptive Graph Convolutional Neural Network and Its Biomedical Applications by Junzhou Huang; Ruoyu Li is a book available to read on EtoBox.

What is Adaptive Graph Convolutional Neural Network and Its Biomedical Applications about?

Graph convolutional neural networks (GCN) are generalizations of classical CNNs to better work with graph-structured data that include biochemical molecular graph, 3D point cloud and social networks. Current convolutional kernels in GCNs were built upon fixed and shared graph structure. However, for most real-world data, graph structure varies in terms of both scale and topology. A generalizable convolutional operator on graph is supposed to be compatible with different graph topologies. In the article, authors introduced a generalized and flexible GCN framework along with a new spectral graph convolutional layer parameterizing distance metric and feature transform. Besides original graph structure, a residual graph is constructed and learned throughout training. Therefore, the introduced Adaptive graph convolutional network (AGCN) is adaptive to graphs of arbitrary topological structure and scale and is also adaptive to various learning tasks easily. With graph attention network, we enabled AGCN to learn graph representation from a bag of patches randomly sampled from large medical images such as Whole-slide-image for sophisticated understanding tasks, for example survival predict

Author

Junzhou Huang; Ruoyu Li

Publisher

Elsevier Science & Technology

Published

2023

Language

EN

ISBN

9780128198728