TY - CHAP
T1 - Deep learning: Generative adversarial networks and adversarial methods
AU - Wolterink, Jelmer M.
AU - Kamnitsas, Konstantinos
AU - Ledig, Christian
AU - Išgum, Ivana
PY - 2019
Y1 - 2019
N2 - Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game. Adversarial techniques have been extensively used to synthesize and analyze biomedical images. This chapter provides an introduction to GANs and adversarial methods, with an overview of biomedical image analysis tasks that have benefited from such methods. We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions.
AB - Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game. Adversarial techniques have been extensively used to synthesize and analyze biomedical images. This chapter provides an introduction to GANs and adversarial methods, with an overview of biomedical image analysis tasks that have benefited from such methods. We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85082621182&origin=inward
U2 - https://doi.org/10.1016/B978-0-12-816176-0.00028-4
DO - https://doi.org/10.1016/B978-0-12-816176-0.00028-4
M3 - Chapter
T3 - Handbook of Medical Image Computing and Computer Assisted Intervention
SP - 547
EP - 574
BT - Handbook of Medical Image Computing and Computer Assisted Intervention
PB - Elsevier
ER -