Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging
نویسندگان
چکیده
In recent years, deep learning techniques have been applied in musculoskeletal radiology to increase the diagnostic potential of acquired images. Generative adversarial networks (GANs), which are neural that can generate or transform images, aid faster imaging by generating images with a high level realism across multiple contrast and modalities from existing protocols. This review introduces key architectures GANs as well their technical background challenges. Key research trends highlighted, including: (a) reconstruction high-resolution MRI; (b) image synthesis different contrasts; (c) enhancement efficiently preserves high-frequency information suitable for human interpretation; (d) pixel-level segmentation annotation sharing between domains; (e) applications anatomies. addition, an overview is provided issues wherein clinical applicability challenging capture conventional performance metrics expert evaluation. When clinically validated, improve imaging. Keywords: Adults Pediatrics, Computer Aided Diagnosis (CAD), Applications-General (Informatics), Informatics, Skeletal-Appendicular, Skeletal-Axial, Soft Tissues/Skin © RSNA, 2021
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ژورنال
عنوان ژورنال: Radiology
سال: 2021
ISSN: ['2638-6135']
DOI: https://doi.org/10.1148/ryai.2021200157