Dual Convolutional Neural Network for Lung Nodule Classification

In some computer vision tasks, in addition to images, we also have a graph indicating the relationship of the images. In this case, we can use Graph Convolutional Network (GCN) to get extra information from the graph. GCN is a widely-used deep neural network with graph convolutional layers extracting high-level feature maps from the graph. In this paper, we design a Dual Convolution Neural Network (Dual-CNN) with a CNN solving the images and a GCN solving the graph. We take the lung nodule classification task as an example to introduce our model. In this task, we have features including a CT image and several categorical attributes for each nodule, and the purpose is to classify if the nodule is malignant. Conventional classifiers take these features as the input and then give the prediction, yet we additionally construct a graph based on attributes and perform graph convolution on it. We also design experiments to demonstrate the effectiveness of our Dual-CNN models. Codes are available on https://github.com/temp2244/lung_nodules_classification.