Large-scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention: 4th International Workshop, Labels 2019, and First International Workshop, Hal-miccai 2019, Held in Conjunction With Miccai 2019, Shenzhen, China, Oct

Zhou, Luping (Editor)/ Heller, Nicholas (Editor)/ Shi, Yiyu (Editor)/ Sznitman, Raphael (Editor)/ Cheplygina, Veronika (Editor)

ISBN 10: 3030336417 / ISBN 13: 9783030336417

Verlag: Springer-Verlag New York Inc, 2019

Beschreibung

176 pages. 9.25x6.10x0.43 inches. In Stock.

Bestandsnummer des Verkäufers x-3030336417

Bibliografische Details

• Titel: Large-scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention: 4th International Workshop, Labels 2019, and First International Workshop, Hal-miccai 2019, Held in Conjunction With Miccai 2019, Shenzhen, China, Oct
• Verlag: Springer-Verlag New York Inc
• Veröffentlichungsdatum: 2019
• Sprache: Englisch
• ISBN-10: 3030336417
• ISBN-13: 9783030336417
• Bindung: Paperback
• Zustand: Brand New

Über diesen Titel

Inhaltsangabe

This book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.

The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications inmedical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection.