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

Kuan Yu Hsieh, Han Chun Tsai, Guan-Yu Chen

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
主出版物子標題Enabling Innovative Technologies for Global Healthcare, EMBC 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2400-2403
頁數4
ISBN(電子)9781728119908
DOIs
出版狀態Published - 20 7月 2020
事件42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, 加拿大
持續時間: 20 7月 202024 7月 2020

出版系列

名字Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2020-July
ISSN(列印)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
國家/地區加拿大
城市Montreal
期間20/07/2024/07/20

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