Graph Convolutional Networks for Cervical Cell Classification

Jun Shi, Ruoyu Wang, Yushan Zheng, Zhiguo Jiang, Lanlan Yu

Abstract: Cervical cell classification is of important clinical significance in the screening of cervical cancer at early stages. In this paper, we present a novel cervical cell clas-sification method based on Graph Convolutional Network (GCN). In contrast with Convolutional Neural Networks (CNN) which can classify cervical cells through learned deep features, the proposed method uses GCN to explore the im-age-level potential relationship for improving the classification performance. Spe-cifically, each cervical cell image is represented by a pre-trained CNN. k-means clustering is performed on these CNN features and then the graph structure is constructed where each node is characterized by one cluster centroid. Conse-quently, the image-level relationship can be captured in terms of intrinsic cluster-ing structure. GCN is applied to propagate the underlying correlation of nodes and the relation-aware representation of GCN is incorporated to enrich the image-level CNN features. Experiments on the cervical cell image datasets demonstrate the effectiveness of our method.

Keywords: Cervical cancer, Cervical Cell Classification, Graph Convolutional Networks

TL;DR: A novel cervical cell classification method based on Graph Convolutional Network (GCN) is proposed in this paper