Application of Adversarial Training in Medical Signals, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor) ; Rohban, Mohammad Hossein (Supervisor)
Abstract
Recent success of Deep Learning models, resulted in their evergrowing application in many fields. However these models usually require huge datasets, which can sometimes be hard to collect. One of the challenges related to medical data, is the Batch Effect; Medical data is usually gathered through multiple experiments. Each experiment might have a slightly different conditions than the other, resulting a shift in the data related to that batch. Batch effects can have more severe impact during testing time, as the shift in the data distribution could be bigger. Many methods have been proposed to reduce or remove the effect of external conditions on data distribution.Deep Learning models have...
Cataloging briefApplication of Adversarial Training in Medical Signals, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor) ; Rohban, Mohammad Hossein (Supervisor)
Abstract
Recent success of Deep Learning models, resulted in their evergrowing application in many fields. However these models usually require huge datasets, which can sometimes be hard to collect. One of the challenges related to medical data, is the Batch Effect; Medical data is usually gathered through multiple experiments. Each experiment might have a slightly different conditions than the other, resulting a shift in the data related to that batch. Batch effects can have more severe impact during testing time, as the shift in the data distribution could be bigger. Many methods have been proposed to reduce or remove the effect of external conditions on data distribution.Deep Learning models have...
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