A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation-Reference-Cited by-同舟云学术

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A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation

Published:2021-06 Issue:6 Volume:25 Page:2029-2040
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Jha Debesh^ORCID,Smedsrud Pia H.^ORCID,Johansen Dag,de Lange Thomas^ORCID,Johansen Havard D.^ORCID,Halvorsen Pal,Riegler Michael A.^ORCID

Funder

Research Council of Norway Project

Norges Forskningsråd

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/6221020/9446651/09314114.pdf?arnumber=9314114

Cited by 185 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Know your orientation: A viewpoint-aware framework for polyp segmentation;Medical Image Analysis;2024-10

2. DEMF-Net: A dual encoder multi-scale feature fusion network for polyp segmentation;Biomedical Signal Processing and Control;2024-10

3. MixUNet: A lightweight medical image segmentation network capturing multidimensional semantic information;Biomedical Signal Processing and Control;2024-10

4. A Multi-Scale Liver Tumor Segmentation Method Based on Residual and Hybrid Attention Enhanced Network with Contextual Integration;Sensors;2024-09-09

5. Multi-view orientational attention network combining point-based affinity for polyp segmentation;Expert Systems with Applications;2024-09

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