A cross-domain conditional GAN generates stereo-consistent hyperrealistic endoscopic images from phantoms, showing better depth perception and realism than baseline image-to-image methods in evaluations by 6 raters.
Repli- cated mitral valve models from real patients offer training opportunities for mini- mally invasive mitral valve repair
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Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training
A cross-domain conditional GAN generates stereo-consistent hyperrealistic endoscopic images from phantoms, showing better depth perception and realism than baseline image-to-image methods in evaluations by 6 raters.