UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

被引：0

作者：

Yu, Xin ^{[1
]}

Yang, Qi ^{[1
]}

Zhou, Yinchi ^{[1
]}

Cai, Leon Y. ^{[2
]}

Gao, Riqiang ^{[1
,3
]}

Lee, Ho Hin ^{[1
]}

Li, Thomas ^{[2
]}

Bao, Shunxing ^{[4
]}

Xu, Zhoubing ^{[3
]}

Lasko, Thomas A. ^{[5
]}

Abramson, Richard G. ^{[2
,6
]}

Zhang, Zizhao ^{[7
]}

Huo, Yuankai ^{[1
,4
]}

Landman, Bennett A. ^{[1
,2
,4
,5
]}

Tang, Yucheng ^{[4
,8
]}

机构：

[1] Department of Computer Science, Vanderbilt University, Nashville,TN,37212, United States

[2] Department of Biomedical Engineering, Vanderbilt University, Nashville,TN,37212, United States

[3] Digital Technology and Innovation, Siemens Healthineers, Princeton,NJ,08540, United States

[4] Department of Electrical and Computer Engineering, Vanderbilt University, Nashville,TN,37212, United States

[5] Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville,TN,37235, United States

[6] Annalise-AI, Pty, Ltd.

[7] Google Cloud AI

[8] Nvidia Corporation, United States

来源：

arXiv | 2022年

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D O I：

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摘要：

Image segmentation - Learning systems - Medical imaging

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