WGAN-Based Synthetic Minority Over-Sampling Technique: Improving Semantic Fine-Grained Classification for Lung Nodules in CT Images

WGAN-Based Synthetic Minority Over-Sampling Technique: Improving Semantic Fine-Grained Classification for Lung Nodules in CT Images

Data imbalance issue generally exists in most medical image analysis problems and maybe getting important with the popularization of data-hungry deep learning paradigms. We explore the cutting-edge Wasserstein generative adversarial networks (WGANs) to address the data imbalance problem with oversam...

Full description

Bibliographic Details
Main Authors: Qingfeng Wang, Xuehai Zhou, Chao Wang, Zhiqin Liu, Jun Huang, Ying Zhou, Changlong Li, Hang Zhuang, Jie-Zhi Cheng
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Computer-aided diagnosis (CAD)
lung nodule
computed tomography (CT)
synthetic minority over-sampling
deep learning
data imbalance
Online Access:https://ieeexplore.ieee.org/document/8629907/

Internet

https://ieeexplore.ieee.org/document/8629907/

Similar Items