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Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks. (arXiv:2205.09842v1 [eess.IV])
cs.CV updates on arXiv.org arxiv.org
Deep learning has a great potential to alleviate diagnosis and prognosis for
various clinical procedures. However, the lack of a sufficient number of
medical images is the most common obstacle in conducting image-based analysis
using deep learning. Due to the annotations scarcity, semi-supervised
techniques in the automatic medical analysis are getting high attention.
Artificial data augmentation and generation techniques such as generative
adversarial networks (GANs) may help overcome this obstacle. In this work, we
present an image generation approach that …
artificial arxiv generation generative adversarial networks images networks