Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
166
Utgivningsdatum
2017-09-08
Upplaga
1st ed. 2017
Förlag
Springer International Publishing AG
Medarbetare
Cardoso, M. Jorge (ed.), Granger, Eric (ed.), Duong, Luc (ed.), Carbonneau, Marc-André (ed.), Albarqouni, Shadi (ed.), Carneiro, Gustavo (ed.), Arbel, Tal (ed.), Lee, Su-Lin (ed.), Cheplygina, Veronika (ed.), Balocco, Simone (ed.), Mateus, Diana (ed.), Zahnd, Guillaume (ed.), Maier-Hein, Lena (ed.),
Illustratör/Fotograf
Bibliographie
Illustrationer
73 Illustrations, black and white; XVI, 166 p. 73 illus.
Dimensioner
234 x 156 x 10 mm
Vikt
268 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783319675336

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, Qubec City, QC, Canada, September 1014, 2017, Proceedings

Häftad,  Engelska, 2017-09-08
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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 Qubec 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.
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