Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model-Reference-Cited by-同舟云学术

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Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model

Published:2023-04-17 Issue:9 Volume:136 Page:1117-1119
ISSN:0366-6999
Container-title:Chinese Medical Journal
language:en
Short-container-title:

Author:

Sun Xiao¹,Xin Yanlu²,Liu Dahai³,Xie Boheng⁴,Wang Yuanyong⁵,Lu Tong⁶,Jiang Gang⁷,Lin Jizheng⁷,Jiao Wenjie¹

Affiliation:

1. Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong 266003, China

2. Department of Nephrology, Affiliated Hospital of Qingdao University, Qingdao, Shandong 266003, China

3. Department of Health Management Center, Affiliated Hospital of Qingdao University, Qingdao, Shandong 266003, China

4. Department of Thoracic Surgery, Tai’an City Central Hospital, Tai’an, Shandong 271000, China

5. Department of Thoracic Surgery, Tangdu Hospital of the Fourth Military Medical University, Xi’an, Shaanxi 710032, China

6. Department of Thoracic Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China

7. Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong 266003, China.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

General Medicine,General Medicine

Reference5 articles.

1. Complete mediastinal lymphadenectomy: the core component of the multidisciplinary therapy in resectable non-small cell lung cancer;Zhong;Eur J Cardiothorac Surg,2008

2. Improvement in the survival of patients with stage IV non-small-cell lung cancer: experience in a single institutional 1995–2017;Takano;Lung Cancer,2019

3. Faster R-CNN: towards real-time object detection with region proposal networks;Ren;IEEE Trans Pattern Anal Mach Intell,2017

4. DeepAnomaly: combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field;Christiansen;Sensors (Basel),2016

5. Alzheimer's disease neuroimaging I. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease;Spasov;Neuroimage,2019

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