Semi-Supervised Adversarial Learning for Improving the Diagnosis of Pulmonary Nodules

Author:

Fu Yu1ORCID,Xue Peng1ORCID,Xiao Taohui1,Zhang Zhili1ORCID,Zhang Youren1,Dong Enqing1ORCID

Affiliation:

1. School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, China

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Science and Technology Small and Medium-sized Enterprises of Shandong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SSAL-Net: Semi-supervised network integrating self-supervised adversarial learning for diagnosing subtypes of pulmonary nodules;Biomedical Signal Processing and Control;2024-10

2. An Anthropomorphic Diagnosis System of Pulmonary Nodules using Weak Annotation-Based Deep Learning;2024-05-05

3. TNCB: Tri-Net With Cross-Balanced Pseudo Supervision for Class Imbalanced Medical Image Classification;IEEE Journal of Biomedical and Health Informatics;2024-04

4. Semi-Supervised Adversarial Transfer Learning for Automated Skin Lesion Segmentation;2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC);2024-01-29

5. Design and Implementation of Lung Abnormality Detection Through Sparse Projection Features Enabled Distributed Convolution Network;2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT);2023-04-05

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