Graph Convolutional Neural Networks for Histological Classification of Pancreatic Cancer

Wu, Weiyi; Li, Xiaoying; Hamilton, Raymond F.; Suriawinata, Arief A.; Hassanpour, Saeed

doi:10.1101/2022.01.26.22269832

Cited by 3 publications

(2 citation statements)

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“…To overcome this difficulty, we separate the feature aggregation step from the neural network model. This approach is similar to models like SGC [20], SIGN [39], etc.…”

Section: Importance Of Hop Featuresmentioning

confidence: 99%

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Feature selection: Key to enhance node classification with graph neural networks

Maurya

Liu²,

Murata

2023

CAAI Trans on Intel Tech

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Graphs help to define the relationships between entities in the data. These relationships, represented by edges, often provide additional context information which can be utilised to discover patterns in the data. Graph Neural Networks (GNNs) employ the inductive bias of the graph structure to learn and predict on various tasks. The primary operation of graph neural networks is the feature aggregation step performed over neighbours of the node based on the structure of the graph. In addition to its own features, for each hop, the node gets additional combined features from its neighbours. These aggregated features help define the similarity or dissimilarity of the nodes with respect to the labels and are useful for tasks like node classification. However, in real-world data, features of neighbours at different hops may not correlate with the node's features. Thus, any indiscriminate feature aggregation by GNN might cause the addition of noisy features leading to degradation in model's performance. In this work, we show that selective aggregation of node features from various hops leads to better performance than default aggregation on the node classification task. Furthermore, we propose a Dual-Net GNN architecture with a classifier model and a selector model. The classifier model trains over a subset of input node features to predict node labels while the selector model learns to provide optimal input subset to the classifier for the best performance. These two models are trained jointly to learn the best subset of features that give higher accuracy in node label predictions. With extensive experiments, we show that our proposed model outperforms both feature selection methods and state-of-the-art GNN models with remarkable improvements up to 27.8%.

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“…To overcome this difficulty, we separate the feature aggregation step from the neural network model. This approach is similar to models like SGC [20], SIGN [39], etc.…”

Section: Importance Of Hop Featuresmentioning

confidence: 99%

“…There are many different ways of aggregation schemes proposed in current GNN literature. Many GNN models use simple indiscriminate aggregation [19–21], some use random sampling of neighbours [22, 23], attention‐based aggregation [24, 25], filter coefficient based aggregation [26–28], etc.…”

Section: Introductionmentioning

confidence: 99%