Extinction arouses attention to the context in a behavioral suppression method with humans.

2013 ◽  
Vol 39 (1) ◽  
pp. 99-105 ◽  
Author(s):  
James Byron Nelson ◽  
Jeffrey A. Lamoureux ◽  
Samuel P. León
2011 ◽  
Vol 131 (9) ◽  
pp. 737-746 ◽  
Author(s):  
Shouji Sugimura ◽  
Tadashi Naitoh ◽  
Atsushi Toyama ◽  
Fumihiko Ohta

Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.


Author(s):  
Junhao Huang ◽  
Fang Fang ◽  
Zhihua Wang ◽  
Mingxuan Hang ◽  
Yibo Wang ◽  
...  

2020 ◽  
pp. 1-1
Author(s):  
Wang Luo ◽  
Jian Ge ◽  
Huan Liu ◽  
Guanzhong Wang ◽  
Bing Jie Bai ◽  
...  

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