Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining-Reference-Cited by-同舟云学术

Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

Published:2023-06 Issue: Volume: Page:
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Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Sun Kexin¹,Chen Zhineng¹,Wang Gongwei²,Liu Jun²,Ye Xiongjun³,Jiang Yu-Gang¹

Affiliation:

1. School of Computer Science & Shanghai, Fudan University,Collaborative Innovation Center of Intelligent Visual Computing

2. Peking University People's Hospital

3. National Cancer Center & National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College,Department of Urology

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203190.pdf?arnumber=10203190

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4. Cbam: Convolutional block attention module;woo;Proceedings of the European Conference on Computer Vision (ECCV),0

5. U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to- image translation;kim;ArXiv Preprint,2019

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