Temporal-spatial Feature Learning of DCE-MR Images via 3DCNN
Code for paper:
Requirements
Python 2.7
TensorFlow == 1.4.0
Keras == 2.2.4
For keras2.0.0 compatibility checkout tag keras2.0.0
Run:
- Start the training using:
python main.py -c configs/fusion_config.json # MCF-3D-CNNpython main.py -c configs/3dcnn_config.json # 3DCNN- Start Tensorboard visualization using:
tensorboard --logdir=experiments/Year-Month-Day/Ex-name/logsData
The proprietary of the data belongs to Beijing Friendship Hospital. You can get access to anonymous data here.
Tensor-based data representation
MCF-3DCNN architecture
Results
Tabel1 The results of discriminating the HCC and cirrhosis
Tabel2 The results of non-invasive assessment of HCC differentiation
Feature maps of C1 and C2 convolution layer
One Vs. Other
A multi-classification problem is transformed into multiple binary classification problems. The results are as follow:
The average area under the ROC curve for 3DCNN for discriminating poorly, moderately and well differentiated HCCs.
Reference
Citation
If you use this code or data for your research, please cite our papers.
@inproceedings{IGTA 2018,
title={Temporal-Spatial Feature Learning of Dynamic Contrast Enhanced-MR Images via 3D Convolutional Neural Networks},
author={Jia X., Xiao Y., Yang D., Yang Z., Wang X., Liu Y},
booktitle={Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science},
year={2018}
}
Da-wei Yang, Xi-bin Jia, Yu-jie Xiao, Xiao-pei Wang, Zhen-chang Wang, and Zheng-han Yang, “Noninvasive Evaluation of the Pathologic Grade of Hepatocellular Carcinoma Using MCF-3DCNN: A Pilot Study,” BioMed Research International, vol. 2019, Article ID 9783106, 12 pages, 2019. https://doi.org/10.1155/2019/9783106.