Tribranchu-Net: A Size-Sensitive Network for Orbital Tumor Segmentation-Reference-Cited by-同舟云学术

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Tribranchu-Net: A Size-Sensitive Network for Orbital Tumor Segmentation

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
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Author:

He Yuchen¹,Wang Wenyu¹,Cheng Zhiming²,Qi Wei³,Duan Zhigang⁴,Jin Kai⁵,Ye Juan⁵,Wang Shuai¹

Affiliation:

1. Shandong University,School of Mechanical, Electrical and Information Engineering,Weihai,China,264209

2. Hangzhou Dianzi University,School of Cyberspace,Hangzhou,China,310018

3. NO.94860 Army of PLA,Nanjing,China,210049

4. Inspur Cloud Information Technology Co., Ltd,Jinan,China,250101

5. Zhejiang University School of Medicine,Department of Ophthalmology, the Second Affiliated Hospital,Hangzhou,China,310009

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230640.pdf?arnumber=10230640

Reference17 articles.

1. Channel Attention Based Iterative Residual Learning for Depth Map Super-Resolution

2. HBNet: Hybrid Blocks Network for Segmentation of Gastric Tumor from Ordinary CT Images

3. Hardnet-mseg: A simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 fps;huang,2021

4. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

5. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs;chen;IEEE Transactions on Pattern Analysis and Machine Intelligence,2017

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