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RAR-U-Net: a Residual Encoder to Attention Decoder by Residual
  Connections Framework for Spine Segmentation under Noisy Labels
v1v2v3v4 (latest)

RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels

27 September 2020
Ziyang Wang
Zhengdong Zhang
Irina Voiculescu
    MedIm
ArXiv (abs)PDFHTML

Papers citing "RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels"

3 / 3 papers shown
Title
Integrating Mamba Sequence Model and Hierarchical Upsampling Network for
  Accurate Semantic Segmentation of Multiple Sclerosis Legion
Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion
Kazi Shahriar Sanjid
Md. Tanzim Hossain
Md. Shakib Shahariar Junayed
M. M. Uddin
Mamba
105
9
0
26 Mar 2024
When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class
  Medical Image Semantic Segmentation
When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation
Ziyang Wang
Tianze Li
Jian-Qing Zheng
Baoru Huang
84
34
0
12 Aug 2022
Triple-View Feature Learning for Medical Image Segmentation
Triple-View Feature Learning for Medical Image Segmentation
Ziyang Wang
Irina Voiculescu
62
11
0
12 Aug 2022
1