Harnessing Multiple Level Features to Improve Segmentation Performance of Deep Neural Network: A Case Study in Magnetic Resonance Imaging of Nasopharyngeal Cancer
Author:
Affiliation:
1. Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
2. School of Computer Engineering, Jiangsu University of Technology, Changzhou, China
Funder
Natural Science Foundation of Jiangsu Province
Changzhou Science and Technology Program
Clinical Application-Oriented Medical Innovation Foundation from the National Clinical Research Center for Orthopedics
Postgraduate Research and Practice Innovation Program of Jiangsu Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/6287639/10380310/10551826.pdf?arnumber=10551826
Reference50 articles.
1. Automatic Tumor Segmentation with Deep Convolutional Neural Networks for Radiotherapy Applications
2. Medical Image Segmentation: A Brief Survey
3. Nasopharyngeal Carcinoma Lesion Segmentation from MR Images by Support Vector Machine
4. Semi-supervised Nasopharyngeal Carcinoma Lesion Extraction from Magnetic Resonance Images Using Online Spectral Clustering with a Learned Metric
5. A hybrid supervised learning nasal tumor discrimination system for DMRI
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