Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Paper Info

Title
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Abstract
•First work to investigate solutions for collaborative medical AI model for COVID-19.•A novel federated semi-supervised learning technique is proposed to train a network.•Method fully utilizes available data from different sources, w/ or w/o annotations.•Multinational dataset of 1704 scans from 3 countries (China, Italy, and Japan).•Studies fully evaluate and understand different configurations of the method.

Year	DOI	Venue
2021	10.1016/j.media.2021.101992	Medical Image Analysis
Keywords	DocType	Volume
COVID-19,Chest CT,Federated learning,Semi-supervision	Journal	70
ISSN	Citations	PageRank
1361-8415	6	0.60
References	Authors
29	20

Authors (20 rows)

Cited by (6 rows)

References (29 rows)

Name	Order	Citations	PageRank
Dong Yang	1	47	11.05
Ziyue Xu	2	597	35.50
Wenqi Li	3	309	20.82
Andriy Myronenko	4	791	29.26
Holger Roth	5	737	45.70
Stephanie Harmon	6	15	2.17
Sheng Xu	7	507	71.47
Baris Turkbey	8	212	23.00
Evrim Turkbey	9	6	0.94
Xiaosong Wang	10	59	9.51
Wentao Zhu	11	250	19.35
Gianpaolo Carrafiello	12	6	0.60
Francesca Patella	13	6	0.94
Maurizio Cariati	14	6	0.60
Hirofumi Obinata	15	6	0.60
Hitoshi Mori	16	6	0.60
Kaku Tamura	17	6	0.60
Peng An	18	6	0.60
Bradford J Wood	19	142	31.69
Daguang Xu	20	50	14.28