Paper Info
Title
LNDb challenge on automatic lung cancer patient management
Abstract
•The LNDb Challenge addressed the automated prediction of patient follow-up in CT.•Nodule detection is challenging but segmentation achieved interobserver variability.•Data collection, training and evaluation of underrepresented classes is essential.•Integration of scan-level information must be considered in future approaches.•Future tools/challenges must focus more on scan-level clinical-oriented goals.
Year
DOI
Venue
2021
10.1016/j.media.2021.102027
Medical Image Analysis
Keywords
DocType
Volume
Lung cancer,Low dose computed tomography,Pulmonary nodules,Follow-up recommendation,Deep learning,Challenge
Journal
70
ISSN
Citations 
PageRank 
1361-8415
0
0.34
References 
Authors
0
26
Name
Order
Citations
PageRank
Joao Pedrosa182.89
Guilherme Aresta2305.58
Carlos Ferreira311.02
Gurraj Atwal400.34
Hady Ahmady Phoulady500.34
Xiaoyu Chen600.34
Rong-Zhen Chen791.51
Jiaoliang Li800.34
Liansheng Wang9188.54
Adrian Galdran1001.01
Hamid Bouchachia1100.34
Krishna Chaitanya Kaluva1200.34
Kiran Vaidhya1300.34
Abhijith Chunduru1400.34
Sambit Tarai1500.34
Sai Prasad Pranav Nadimpalli1600.34
Suthirth Vaidya1700.34
Ildoo Kim18122.76
Alexandr Rassadin1900.34
Zhenhuan Tian2000.34
Zhongwei Sun2100.34
Yizhuan Jia2200.34
Xuejun Men2300.34
Isabel Ramos2400.34
A. Cunha2522.76
Aurélio J. C. Campilho2632140.49