Large-scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention : International Workshops, Labels 2019, Hal-miccai 2019, and Curious 2019, Held in Conjunction With Miccai 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings

Zhou, Luping (EDT); Heller, Nicholas (EDT); Shi, Yiyu (EDT); Xiao, Yiming (EDT); Sznitman, Raphael (EDT)

ISBN 10: 3030336417 / ISBN 13: 9783030336417

Editore: Springer, 2019

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Dati bibliografici

• Titolo: Large-scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention : International Workshops, Labels 2019, Hal-miccai 2019, and Curious 2019, Held in Conjunction With Miccai 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings
• Editore: Springer
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030336417
• ISBN 13: 9783030336417
• Rilegatura: Brossura
• Condizione: As New

Descrizione articolo

Riassunto

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.