Harnessing Multiple Level Features to Improve Segmentation Performance of Deep Neural Network: A Case Study in Magnetic Resonance Imaging of Nasopharyngeal Cancer

Harnessing Multiple Level Features to Improve Segmentation Performance of Deep Neural Network: A Case Study in Magnetic Resonance Imaging of Nasopharyngeal Cancer

The lesion area of cancer presents complex physiological structures, posing significant challenges for accurate segmentation, which is required in radiotherapy or chemo-radiotherapy. The existing general-purpose segmentation models have made significant progress, but their segmentation performance r...

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Bibliographic Details
Main Authors: Rongzhi Mao, Liangxu Xie, Xiaohua Lu, Jialu Pei, Xiaojun Xu, Shan Chang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep neural networks
U-Net
multi-level features
nasopharyngeal carcinoma
Online Access:https://ieeexplore.ieee.org/document/10551826/
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https://ieeexplore.ieee.org/document/10551826/

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