Deep neural networks such as CNNs are “black-box”, therefore various interpretability methods have been developed to understand which aspects of the input data drive the decisions of the network.

Text GCN is a model which allows us to use a graph neural network for text classification where the type of network is convolutional. The below figure is a representation of the adaptation of ...

This project is an open source implementation of the graph convolutional neural network for classification of building group patterns using spatial vector data. The initial aim of our work is to ...

To solve this issue, in this article, we proposed a graph-in-graph (GiG) model and a related GiG convolutional network (GiGCN) for HSI classification from a superpixel viewpoint. The GiG ...

The traditional convolutional neural network (CNN) is not suitable for such classification problems. Therefore graph-oriented convolution operations need to be introduced, and spectral-GCN is one of ...

Breast cancer is one of the diseases with the highest incidence and mortality among women in the world, which has posed a serious threat to women’s health. The appearance of clustered calcifications ...