國立虎尾科技大學 |

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 /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention/ 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.
Reminder of title:	International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings /
other author:	Zhou, Luping.
Description:	XX, 154 p. 62 illus., 48 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Optical data processing. -
Online resource:	https://doi.org/10.1007/978-3-030-33642-4
ISBN:	9783030336424

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 /
Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted InterventionInternational Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings /[electronic resource] :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. - 1st ed. 2019. - XX, 154 p. 62 illus., 48 illus. in color.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;11851. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

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.

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.

ISBN: 9783030336424

Standard No.: 10.1007/978-3-030-33642-4doiSubjects--Topical Terms:

639187
Optical data processing.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

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 /
LDR:05042nam a22004335i 4500 001 1006681
003 DE-He213
005 20200902192021.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030336424 $9 978-3-030-33642-4
024 7 $a 10.1007/978-3-030-33642-4 $2 doi
035 $a 978-3-030-33642-4
050 4 $a TA1630-1650
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.6 $2 23
082 0 4 $a 006.37 $2 23
245 1 0 $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.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XX, 154 p. 62 illus., 48 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 11851
505 0 $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.
520 $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.
650 0 $a Optical data processing. $3 639187
650 0 $a Artificial intelligence. $3 559380
650 0 $a Health informatics. $3 1064466
650 1 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Health Informatics. $3 593963
700 1 $a Zhou, Luping. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1069370
700 1 $a Heller, Nicholas. $e editor. $1 https://orcid.org/0000-0001-8516-8707 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300285
700 1 $a Shi, Yiyu. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300286
700 1 $a Xiao, Yiming. $e editor. $1 https://orcid.org/0000-0002-0962-3525 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1279531
700 1 $a Sznitman, Raphael. $e editor. $1 https://orcid.org/0000-0001-6791-4753 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1283759
700 1 $a Cheplygina, Veronika. $e editor. $1 https://orcid.org/0000-0003-0176-9324 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1283765
700 1 $a Mateus, Diana. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1273639
700 1 $a Trucco, Emanuele. $e editor. $1 https://orcid.org/0000-0002-5055-0794 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1282890
700 1 $a Hu, X. Sharon. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300287
700 1 $a Chen, Danny. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300288
700 1 $a Chabanas, Matthieu. $e editor. $1 https://orcid.org/0000-0003-1690-3709 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1279535
700 1 $a Rivaz, Hassan. $e editor. $1 https://orcid.org/0000-0001-5800-3034 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1279533
700 1 $a Reinertsen, Ingerid. $e editor. $1 https://orcid.org/0000-0003-0999-3849 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1279534
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030336417
776 0 8 $i Printed edition: $z 9783030336431
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-030-33642-4
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)