Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging

Author:

Kaji Shizuo,Kida Satoshi

Funder

JST PRESTO

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine,Radiation

Reference69 articles.

1. Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, Summers RM, Giger ML. Deep learning in medical imaging and radiation therapy. Med phys. 2018;46:e1–36.

2. Kaji S. Image translation by CNNs trained on unpaired data. 2019. https://github.com/shizuo-kaji/UnpairedImageTranslation . Accessed 18 June 2019.

3. Kaji S. Image translation for paired image datasets (automap + pix2pix). 2019. https://github.com/shizuo-kaji/PairedImageTranslation . Accessed 18 June 2019.

4. Lu L, Zheng Y, Carneiro G, Yang L, editors. Deep learning and convolutional neural networks for medical image computing—precision medicine, high performance and large-scale datasets. Advances in computer vision and pattern recognition. Springer; 2017.

5. Knoll F, Maier AK, Rueckert D. editors. Machine learning for medical image reconstruction—first international workshop, MLMIR 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings, vol. 11074 of Lecture Notes in Computer Science, Springer; 2018.

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