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Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Interventio

by Luping Zhou
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Current price ₹3,672.00
Original price ₹5,649.00
Original price ₹5,649.00
Original price ₹5,649.00
(-35%)
₹3,672.00
Current price ₹3,672.00

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Book cover type: Paperback
  • ISBN13: 9783030336417
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 154
  • Original Price: EUR 49.99
  • Language: English
  • Edition: 2019
  • Item Weight: 275 grams
  • BISAC Subject(s): Software Development & Engineering / Computer Graphics, Artificial Intelligence / Computer Vision & Pattern Recognition, and Informatics

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.


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