SERU: A cascaded SE‐ResNeXT U‐Net for kidney and tumor segmentation-Reference-Cited by-同舟云学术

SERU: A cascaded SE‐ResNeXT U‐Net for kidney and tumor segmentation

Published:2020-03-23 Issue:14 Volume:32 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Xie Xiuzhen¹,Li Lei²^ORCID,Lian Sheng²,Chen Shaohao³,Luo Zhiming⁴

Affiliation:

1. College of Mathematics and Information EngineeringLongyan University Longyan China

2. Artificial Intelligence DepartmentXiamen University Xiamen China

3. The First Affiliated Hospital of Fujian Medical University Fuzhou China

4. Post‐doctoral Mobile Station of Information and Communication EngineeringXiamen University Xiamen China

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5738

Reference33 articles.

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3. LinT‐Y GoyalP GirshickR HeK DollárP. Focal loss for dense object detection. Paper presented at: Proceedings of the IEEE International Conference on Computer Vision;2017:2980–2988.

4. HuJ ShenL SunG. Squeeze‐and‐excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition;2018:7132–7141.

5. XieS GirshickR DollárP TuZ HeK. Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition;2017:1492–1500.

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