Staging of Hepatocellular Carcinoma Using Deep Feature in Contrast-Enhanced MR Images
- 10.2991/iccia-17.2017.30How to use a DOI?
- Hepatocelluler Carcinoma, Deep Feature, Convolutional Neural Network, Ensemble.
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.
- © 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/).
Cite this article
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 -