Paper Info
Title
Cascrnn-Gl-Net: Cascaded Convolutional And Recurrent Neural Networks With Global And Local Pathways For Classification Of Focal Liver Lesions In Multi-Phase Ct Images
Abstract
Automatic focal liver lesion (FLL) classification based on multiphase computed tomography (CT) images is one of the most crucial components of a computer-aided diagnosis (CAD) system for liver disease. Though much research has been conducted in this field, two challenges still remain: (1) difficulty of representing the temporal enhancement pattern effectively and (2) the need to obtain the local and global information of FLLs. However, most of existing studies only focus on local or global information. In this paper, we propose a cascaded convolutional neural network (CNN) and recurrent neural network (RNN) with global and local pathways, called CasCRNN-GL-Net, for the classification of FLLs in multiphase CT images. The CNN with global and local pathways is used to extract both global and local information. A bi-directional long short-term memory (BD-LSTM) model is employed to represent the temporal enhancement pattern in multi-phase CT images. Moreover, we propose a novel joint loss function to train the proposed framework. The joint loss function is composed of interclass and intra-class losses, which can improve the robustness of the framework. The proposed method outperformed state-of-theart approaches by achieving a mean accuracy of 87.64%. We have released the code in the GitHub (https://github.com/UpCoder/GL_BD_LSTM).
Year
DOI
Venue
2020
10.4310/cis.2020.v20.n4.a2
COMMUNICATIONS IN INFORMATION AND SYSTEMS
DocType
Volume
Issue
Journal
20
4
ISSN
Citations 
PageRank 
1526-7555
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Liang Dong132652.32
Yingying Xu283.62
Lanfen Lin37824.70
Nan Zhou410.72
Hongjie Hu54110.45
Qiaowei Zhang600.34
Qingqing Chen741.88
Xian-Hua Han810928.28
Yutaro Iwamoto91317.95
Yen-Wei Chen10720155.73