Synthesis of CT images from digital body phantoms using CycleGAN [dataset]
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size. We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["CT synthesis","CycleGAN","Generative Adversial Networks","physical modeling","simulation based deep learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size. We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size. We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms
CT synthesis; CycleGAN; Generative Adversial Networks; physical modeling; simulation based deep learning