國立高雄大學圖資館 |

Language: English

Back

Uncertainty for safe utilization of ...

(1998 :)

Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based proceduresfirst International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based proceduresedited by Hayit Greenspan ... [et al.].
Reminder of title:	first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
remainder title:	UNSURE 2019
other author:	Greenspan, Hayit.
corporate name:
Published:	Cham :Springer International Publishing :2019.
Description:	xvii, 192 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Diagnostic imagingCongresses.Data processing
Online resource:	https://doi.org/10.1007/978-3-030-32689-0
ISBN:	9783030326890$q(electronic bk.)

Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based proceduresfirst International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based proceduresfirst International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /[electronic resource] :UNSURE 2019edited by Hayit Greenspan ... [et al.]. - Cham :Springer International Publishing :2019. - xvii, 192 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,118400302-9743 ;. - Lecture notes in computer science ;4891..

UNSURE 2019: Uncertainty quantification and noise modelling -- Probabilistic Surface Reconstruction with Unknown Correspondence -- Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty -- Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference -- Reg R-CNN: Lesion Detection and Grading under Noisy Labels -- Fast Nonparametric Mutual Information based Registration and Uncertainty Estimation -- Quantifying Uncertainty of deep neural networks in skin lesion classification -- UNSURE 2019: Domain shift robustness -- A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data -- Out of distribution detection for intra-operative functional imaging -- CLIP 2019 -- A Clinical Measuring Platform for Building the Bridge across the Quantification of Pathological N-cells in Medical Imaging for Studies of Disease -- Spatiotemporal statistical model of anatomical landmarks on a human embryonic brain -- Spaciousness filters for non-contrast CT volume segmentation of the intestine region for emergency ileus diagnosis -- Recovering physiological changes in nasal anatomy with confidence estimates -- Synthesis of Medical Images Using GANs -- DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation -- Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging -- Data Augmentation from Sketch -- An automated CNN-based 3D anatomical landmark detection method to facilitate surface-based 3D facial shape analysis -- A Device-independent Novel Statistical Modeling for Cerebral TOF-MRA data Segmentation -- Three-dimensional face reconstruction from uncalibrated photographs: application to early detection of genetic syndromes.

This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.

ISBN: 9783030326890$q(electronic bk.)

Standard No.: 10.1007/978-3-030-32689-0doiSubjects--Topical Terms:

445765
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: RC78.7.D53 / U57 2019

Dewey Class. No.: 616.0754

Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based proceduresfirst International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
LDR:04184nmm a2200385 a 4500 001 567957
003 DE-He213
005 20191026142147.0
006 m d
007 cr nn 008maaau
008 200611s2019 sz s 0 eng d
020 $a 9783030326890$q(electronic bk.)
020 $a 9783030326883$q(paper)
024 7 $a 10.1007/978-3-030-32689-0 $2 doi
035 $a 978-3-030-32689-0
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D53 $b U57 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 616.0754 $2 23
090 $a RC78.7.D53 $b U59 2019
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures $h [electronic resource] : $b first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings / $c edited by Hayit Greenspan ... [et al.].
246 3 $a UNSURE 2019
246 3 $a CLIP 2019
246 3 $a MICCAI 2019
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xvii, 192 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 11840
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a UNSURE 2019: Uncertainty quantification and noise modelling -- Probabilistic Surface Reconstruction with Unknown Correspondence -- Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty -- Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference -- Reg R-CNN: Lesion Detection and Grading under Noisy Labels -- Fast Nonparametric Mutual Information based Registration and Uncertainty Estimation -- Quantifying Uncertainty of deep neural networks in skin lesion classification -- UNSURE 2019: Domain shift robustness -- A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data -- Out of distribution detection for intra-operative functional imaging -- CLIP 2019 -- A Clinical Measuring Platform for Building the Bridge across the Quantification of Pathological N-cells in Medical Imaging for Studies of Disease -- Spatiotemporal statistical model of anatomical landmarks on a human embryonic brain -- Spaciousness filters for non-contrast CT volume segmentation of the intestine region for emergency ileus diagnosis -- Recovering physiological changes in nasal anatomy with confidence estimates -- Synthesis of Medical Images Using GANs -- DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation -- Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging -- Data Augmentation from Sketch -- An automated CNN-based 3D anatomical landmark detection method to facilitate surface-based 3D facial shape analysis -- A Device-independent Novel Statistical Modeling for Cerebral TOF-MRA data Segmentation -- Three-dimensional face reconstruction from uncalibrated photographs: application to early detection of genetic syndromes.
520 $a This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
650 0 $a Diagnostic imaging $x Data processing $v Congresses. $3 445765
650 0 $a Artificial intelligence $x Medical applications $v Congresses. $3 442992
650 0 $a Machine learning $v Congresses. $3 384498
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Health Informatics. $3 274212
700 1 $a Greenspan, Hayit. $3 560366
710 2 $a SpringerLink (Online service) $3 273601
711 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
773 0 $t Springer eBooks
830 0 $a Lecture notes in computer science ; $v 4891. $3 383229
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 823073
856 4 0 $u https://doi.org/10.1007/978-3-030-32689-0
950 $a Computer Science (Springer-11645)