Semi-Supervised Learning of Visual Attributes for Automated Assessment of Lung Nodule Malignancy-Reference-Cited by-同舟云学术

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Semi-Supervised Learning of Visual Attributes for Automated Assessment of Lung Nodule Malignancy

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

Chen Liyun¹,Yao Linlin¹,Wang Qian²,Xue Zhong³

Affiliation:

1. Shanghai Jiao Tong University,School of Biomedical Engineering,Shanghai,China,20030

2. ShanghaiTech University,School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices,Shanghai,China,201210

3. Shanghai United Imaging Intelligence Co., Ltd,Shanghai,China,200230

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635131.pdf?arnumber=10635131

Reference19 articles.

1. LUNGx Challenge for computerized lung nodule classification

2. Feature fusion for lung nodule classification

3. 3D multi-view convolutional neural networks for lung nodule classification

4. Lung Nodule Classification Using Deep Features in CT Images

5. Pulmonary nodule classification with deep residual networks

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