Source-Free Semi-Supervised Domain Adaptation for Tuberculosis Recognition-Reference-Cited by-同舟云学术

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Source-Free Semi-Supervised Domain Adaptation for Tuberculosis Recognition

Published:2024-05-27 Issue: Volume:34 Page:1-5
ISSN:
Container-title:2024 IEEE International Symposium on Biomedical Imaging (ISBI)
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Author:

Ma Jie¹,Xu Zhijun¹,Xiong Xinyu¹,NiJiaTi MaYiDiLi²,Zhang Dongyu¹,Sun Weijun³,Gao Feng⁴,Li Guanbin¹

Affiliation:

1. Sun Yat-sen University,School of Computer Science and Engineering,Guangzhou,China

2. The First People’s Hospital of Kashi (Kashgar),Department of Radiology,Prefecture,China

3. Guangdong University of Technology,Guangzhou,China

4. Sun Yat-sen University,The Sixth Affiliated Hospital,Department of General Surgery (Department of Colorectal Surgery),Guangzhou,China

Funder

National Natural Science Foundation of China

Sun Yat-sen University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635111.pdf?arnumber=10635111

Reference22 articles.

1. Chest radiography in tuberculosis detection: summary of current who recommendations and guidance on programmatic approaches,2016

2. Artificial Intelligence Assisting the Early Detection of Active Pulmonary Tuberculosis From Chest X-Rays: A Population-Based Study

3. Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis

4. Self-Rating Curriculum Learning for Localization and Segmentation of Tuberculosis on Chest Radiograph

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