Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 1st ed. 2017 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 Image Processing, Computer Vision, Pattern Recognition, and Graphics Series
Coordonnateurs : Cardoso M. Jorge, Arbel Tal, Lee Su-Lin, Cheplygina Veronika, Balocco Simone, Mateus Diana, Zahnd Guillaume, Maier-Hein Lena, Demirci Stefanie, Granger Eric, Duong Luc, Carbonneau Marc-André, Albarqouni Shadi, Carneiro Gustavo
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.
Includes supplementary material: sn.pub/extras
Date de parution : 09-2017
Ouvrage de 166 p.
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 52,74 €
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Mots-clés :
artificial intelligence; classification; classifiers; computer aided design; computer vision; computerized tomography; image analysis; image enhancement; image reconstruction; image segmentation; imaging systems; learning algorithms; learning systems; machine learning; medical imaging; neural networks; segmentation methods; supervised learning; Support Vector Machines (SVM)
