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In this paper, we introduce U-Net v2, a new robust and efficient U-Net variant for medical image segmentation.
“Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer,”
Juan Silva, Aymeric Histace, Olivier Romain, Xavier Dray, and Bertrand Granado, · 2014
Earlier work this paper cites.
“Fully convolutional networks for semantic segmentation,”
Jonathan Long, Evan Shelhamer, and Trevor Darrell, · 2015
Earlier work this paper cites.
“U-Net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
Earlier work this paper cites.
“WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,”
Jorge Bernal, F Javier Sánchez, Gloria Fernández-Esparrach, Debora Gil, Cristina Rodríguez, and Fernando Vilariño, · 2015
Earlier work this paper cites.
“Automated polyp detection in colonoscopy videos using shape and context information,”
Nima Tajbakhsh, Suryakanth R Gurudu, and Jianming Liang, · 2015
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Pyramid scene parsing network,”
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia, · 2017
Earlier work this paper cites.
“ISIC 2017-skin lesion analysis towards melanoma detection,”
Matt Berseth, · 2017
Earlier work this paper cites.
“A benchmark for endoluminal scene segmentation of colonoscopy images,”
David Vázquez, Jorge Bernal, F Javier Sánchez, Gloria Fernández-Esparrach, Antonio M López, Adriana Romero, Michal Drozdzal, Aaron Courville, et al., · 2017
Cited alongside, same era.
“Path aggregation network for instance segmentation,”
Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia, · 2018
Cited alongside, same era.
“UNet++: A nested U-Net architecture for medical image segmentation,”
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang, · 2018
Cited alongside, same era.
“MDU-Net: Multi-scale densely connected U-Net for biomedical image segmentation,”
Jiawei Zhang, Yuzhen Jin, Jilan Xu, Xiaowei Xu, and Yanchun Zhang, · 2018
Cited alongside, same era.
“CBAM: Convolutional block attention module,”
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon, · 2018
Cited alongside, same era.
“Kvasir-SEG: A segmented polyp dataset,”
Debesh Jha, Pia H Smedsrud, Michael A Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, and Håvard D Johansen, · 2020
Later among the works it cites.
“Shallow attention network for polyp segmentation,”
Jun Wei, Yiwen Hu, Ruimao Zhang, Zhen Li, S Kevin Zhou, and Shuguang Cui, · 2021
Later among the works it cites.
“TransFuse: Fusing Transformers and CNNs for medical image segmentation,”
Yundong Zhang, Huiye Liu, and Qiang Hu, · 2021
Later among the works it cites.
“Polyp-PVT: Polyp segmentation with Pyramid Vision Transformers,”
Bo Dong, Wenhai Wang, Deng-Ping Fan, Jinpeng Li, Huazhu Fu, and Ling Shao, · 2021
Later among the works it cites.
“Pyramid Vision Transformer: A versatile backbone for dense prediction without convolutions,”
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao, · 2021
Later among the works it cites.
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Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al., · 2019
Cited alongside, same era.
“PraNet: Parallel reverse attention network for polyp segmentation,”
Deng-Ping Fan, Ge-Peng Ji, Tao Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, and Ling Shao, · 2020
Cited alongside, same era.
“MALUNet: A multi-attention and light-weight UNet for skin lesion segmentation,”
Jiacheng Ruan, Suncheng Xiang, Mingye Xie, Ting Liu, and Yuzhuo Fu, · 2022
Later among the works it cites.
“EGE-UNet: An efficient group enhanced UNet for skin lesion segmentation,”
Jiacheng Ruan, Mingye Xie, Jingsheng Gao, Ting Liu, and Yuzhuo Fu, · 2023
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