Generation of High-resolution Lung Computed Tomography Images using Generative Adversarial Networks

Kuan Yu Hsieh, Han Chun Tsai, Guan Yu Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To deal with the limiting data in training for new deep learning modules, we purpose a method to generate high-resolution medical images by implementing generative adversarial networks (GAN) models. Firstly, the boundary equilibrium generative adversarial networks model was used to generate the whole lung computed tomography images. Image inpainting was then integrated to generate the delicate details of the lung part by dividing into a coarse network and a refinement network to inpaint more completed and intricate details. With this method, we aim to increase the amount of high-resolution medical images for future applications in deep learning.

Original languageEnglish
Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEnabling Innovative Technologies for Global Healthcare, EMBC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2400-2403
Number of pages4
ISBN (Electronic)9781728119908
DOIs
StatePublished - 20 Jul 2020
Event42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Duration: 20 Jul 202024 Jul 2020

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2020-July
ISSN (Print)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
CountryCanada
CityMontreal
Period20/07/2024/07/20

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