Staging of Hepatocellular Carcinoma Using Deep Feature in Contrast-Enhanced MR Images

DOI

10.2991/iccia-17.2017.30How to use a DOI?

Keywords

Hepatocelluler Carcinoma, Deep Feature, Convolutional Neural Network, Ensemble.

Abstract

Clinical stage of hepatocellular carcinoma (HCC) is of great significance for prognosis. Texture fea-tures of HCC in Contrast-enhanced MR images have been effective for predictions of staging. However, texture features are low-level features, which are usually insufficient to capture the com-plicated characteristics of HCCs. Recently, some studies have been dedicated to learning features in a data driven way for predictions. In this study, we use deep learning that can extract high-level features in order to more accurately staging HCCs. Experimental results demonstrate that deep fea-ture outperforms traditional texture features for HCC staging, and ensembles of deep features de-rived from multiview observations of HCCs yield best results.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qiyao Wang
AU  - Dashun Que
PY  - 2016/07
DA  - 2016/07
TI  - Staging of Hepatocellular Carcinoma Using Deep Feature in Contrast-Enhanced MR Images
BT  - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 186
EP  - 189
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.30
DO  - 10.2991/iccia-17.2017.30
ID  - Wang2016/07
ER  -