1 research outputs found
Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network
Cervical cancer is the second most prevalent cancer affecting women today. As
the early detection of cervical carcinoma relies heavily upon screening and
pre-clinical testing, digital cervicography has great potential as a primary or
auxiliary screening tool, especially in low-resource regions due to its low
cost and easy access. Although an automated cervical dysplasia detection system
has been desirable, traditional fully-supervised training of such systems
requires large amounts of annotated data which are often labor-intensive to
collect. To alleviate the need for much manual annotation, we propose a novel
graph convolutional network (GCN) based semi-supervised classification model
that can be trained with fewer annotations. In existing GCNs, graphs are
constructed with fixed features and can not be updated during the learning
process. This limits their ability to exploit new features learned during graph
convolution. In this paper, we propose a novel and more flexible GCN model with
a feature encoder that adaptively updates the adjacency matrix during learning
and demonstrate that this model design leads to improved performance. Our
experimental results on a cervical dysplasia classification dataset show that
the proposed framework outperforms previous methods under a semi-supervised
setting, especially when the labeled samples are scarce.Comment: ISBI 202