Generation of synthetic ground glass nodules using generative adversarial networks (GANs)

Generation of synthetic ground glass nodules using generative adversarial networks (GANs)

Abstract Background Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs). Methods We used 216 computed tomography images with 340 GGNs fro...

Full description

Bibliographic Details
Main Authors: Zhixiang Wang, Zhen Zhang, Ying Feng, Lizza E. L. Hendriks, Razvan L. Miclea, Hester Gietema, Janna Schoenmaekers, Andre Dekker, Leonard Wee, Alberto Traverso
Format: Article
Language:English
Published: SpringerOpen 2022-11-01
Series:European Radiology Experimental
Subjects:
Deep learning
Tomography (x-ray computed)
Lung
Neural networks (computer)
Solitary pulmonary nodule
Online Access:https://doi.org/10.1186/s41747-022-00311-y

Internet

https://doi.org/10.1186/s41747-022-00311-y

Similar Items