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Standardizing CT Image using Generative Adversarial Network

Speaker:

Jin Chen, Ph.D.,
Associate Professor, Institute of Biomedical Informatics,
Department of Internal Medicine, Department of Computer Science,
University of Kentucky

Abstract:

CT acquisition parameter customization forms a barrier for large-scale CT image analysis, in that capturing CT images with non-standardized imaging protocols is common and it often results in significantly different radiomics features, even for the same patient. To overcome the barrier, we present a new generative adversarial network (GAN) model to learn from a large amount of training CT images and to generate synthetic CT images such that the differences in radiomic features due to using non-standardized imaging protocols are minimized. 

Registration is required. Email ccts@uky.edu by Monday, June 11, 2018. If you require special physical arrangements to attend, please call 859-323-8545.