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|a 9783030336424
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|a 10.1007/978-3-030-33642-4
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|a Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention
|h [electronic resource] :
|b International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings /
|c edited by Luping Zhou, Nicholas Heller, Yiyu Shi, Yiming Xiao, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, X. Sharon Hu, Danny Chen, Matthieu Chabanas, Hassan Rivaz, Ingerid Reinertsen.
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|a 1st ed. 2019.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2019.
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|a XX, 154 p. 62 illus., 48 illus. in color.
|b online resource.
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|a Image Processing, Computer Vision, Pattern Recognition, and Graphics ;
|v 11851
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|a 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2019) -- Comparison of active learning strategies applied to lung nodule segmentation in CT scans -- Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation -- XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosis -- Data Augmentation based on Substituting Regional MRI Volume Scores -- Weakly supervised segmentation from extreme points -- Exploring the Relationship between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks -- DeepIGeoS-V2: Deep Interactive Segmentation of Multiple Organs from Head and Neck Images with Lightweight CNNs -- The Role of Publicly Available Data in MICCAI Papers from 2014 to 2018 -- First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019) -- Hardware Acceleration of Persistent Homology Computation -- Deep Compressed Pneumonia Detection for Low-Power Embedded Devices -- D3MC: A Reinforcement Learning based Data-driven Dyna Model Compression -- An Analytical Method of Automatic Alignment for Electron Tomography -- Fixed-Point U-Net Quantization for Medical Image Segmentation -- Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2019) -- Registration of ultrasound volumes based on Euclidean distance transform -- Landmark-based evaluation of a block-matching registration framework on the RESECT pre- and intra-operative brain image data set -- Comparing deep learning strategies and attention mechanisms of discrete registration for multimodal image-guided interventions.
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|a 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 in medical 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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|a Optical data processing.
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|a Artificial intelligence.
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|a Health informatics.
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|a Image Processing and Computer Vision.
|0 http://scigraph.springernature.com/things/product-market-codes/I22021
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|a Artificial Intelligence.
|0 http://scigraph.springernature.com/things/product-market-codes/I21000
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|a Health Informatics.
|0 http://scigraph.springernature.com/things/product-market-codes/I23060
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|a Zhou, Luping.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Heller, Nicholas.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Shi, Yiyu.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Xiao, Yiming.
|e editor.
|0 (orcid)0000-0002-0962-3525
|1 https://orcid.org/0000-0002-0962-3525
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Sznitman, Raphael.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Cheplygina, Veronika.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Mateus, Diana.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Trucco, Emanuele.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Hu, X. Sharon.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Chen, Danny.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Chabanas, Matthieu.
|e editor.
|0 (orcid)0000-0003-1690-3709
|1 https://orcid.org/0000-0003-1690-3709
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Rivaz, Hassan.
|e editor.
|0 (orcid)0000-0001-5800-3034
|1 https://orcid.org/0000-0001-5800-3034
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a Reinertsen, Ingerid.
|e editor.
|0 (orcid)0000-0003-0999-3849
|1 https://orcid.org/0000-0003-0999-3849
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783030336417
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|i Printed edition:
|z 9783030336431
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|a Image Processing, Computer Vision, Pattern Recognition, and Graphics ;
|v 11851
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|u https://doi.org/10.1007/978-3-030-33642-4
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|a Computer Science (Springer-11645)
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