https://www.selleckchem.com/products/ms-275.html
Generative adversarial network (GAN)-based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches. The aim of this study was to investigate and validate the unsupervised synthesis of highly realistic body computed tomography (CT) images by using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data. We trained the PGGAN by using 11,755 body CT scans. Ten radiologists (4 radiologists
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