TY  - BOOK
AU  - Cardoso,M.Jorge
AU  - Arbel,Tal
AU  - Lee,Su-Lin
AU  - Cheplygina,Veronika
AU  - Balocco,Simone
AU  - Mateus,Diana
AU  - Zahnd,Guillaume
AU  - Maier-Hein,Lena
AU  - Demirci,Stefanie
AU  - Granger,Eric
AU  - Duong,Luc
AU  - Carbonneau,Marc-André
AU  - Albarqouni,Shadi
AU  - Carneiro,Gustavo
ED  - SpringerLink (Online service)
TI  - Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis: 6th Joint International Workshops, CVII-STENT 2017 and Second International Workshop, LABELS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10–14, 2017, Proceedings 
T2  - Image Processing, Computer Vision, Pattern Recognition, and Graphics
SN  - 9783319675343
AV  - TA1630-1650 
U1  - 006.6 23
PY  - 2017///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Optical data processing
KW  - Health informatics
KW  - Artificial intelligence
KW  - Computer organization
KW  - Image Processing and Computer Vision
KW  - Health Informatics
KW  - Artificial Intelligence
KW  - Computer Systems Organization and Communication Networks
N1  - Available to subscribing member institutions only. Доступно лише організаціям членам підписки
N2  - This book constitutes the refereed joint proceedings of the 6th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017, and the Second International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 6 full papers presented at CVII-STENT 2017 and the 11 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing
UR  - https://doi.org/10.1007/978-3-319-67534-3
ER  -