Quasi-supervised learning for super-resolution PET

Author:

Yang Guangtong,Li Chen,Yao Yudong,Wang Ge,Teng Yueyang

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference41 articles.

1. Bulat, A., Yang, J., Tzimiropoulos, G., 2018. To learn image super-resolution, use a GAN to learn how to do image degradation first. In: Proc. Eur. Conf. Comput. Vis.. ECCV, pp. 185–200.

2. Low-dose CT with a residual encoder-decoder convolutional neural network;Chen;IEEE Trans. Med. Imaging,2017

3. Image super-resolution using deep convolutional networks;Dong;IEEE Trans. Pattern Anal. Mach. Intell.,2015

4. Transformer and GAN-Based super-resolution reconstruction network for medical images;Du;Tsinghua Sci. Technol.,2024

5. Glorot, X., Bengio, Y., 2010. Understanding the difficulty of training deep feedforward neural networks. In: Proc. Int. Conf. Artif. Intell. Stat.. pp. 249–256.

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