國立虎尾科技大學 |

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis = Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis/ edited by Carole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, Sarah Parisot.
Reminder of title:	Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /
other author:	Sudre, Carole H.
Description:	XVII, 222 p. 85 illus., 76 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-60365-6
ISBN:	9783030603656

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis = Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image AnalysisSecond International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /[electronic resource] :edited by Carole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, Sarah Parisot. - 1st ed. 2020. - XVII, 222 p. 85 illus., 76 illus. in color.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;12443. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

UNSURE 2020 -- Image registration via stochastic gradient Markov chain Monte Carlo -- RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification -- Hierarchical brain parcellation with uncertainty -- Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-Class Segmentation -- Uncertainty Estimation in Landmark Localization based on Gaussian Heatmaps -- Weight averaging impact on the uncertainty of retinal artery-venous segmentation -- Improving Pathological Distribution Measurements with Bayesian Uncertainty -- Improving Reliability of Clinical Models using Prediction Calibration -- Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior -- Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability -- GRAIL 2020 -- Clustering-based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates -- Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences -- Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders -- Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Proling -- Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation -- Min-cut Max-flow for Network Abnormality Detection: Application to Preterm Birth -- Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface -- The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification -- Intraoperative Liver Surface Completion with Graph Convolutional VAE -- HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification.

This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 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. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.

ISBN: 9783030603656

Standard No.: 10.1007/978-3-030-60365-6doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis = Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /
LDR:04612nam a22004095i 4500 001 1030166
003 DE-He213
005 20201205195110.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030603656 $9 978-3-030-60365-6
024 7 $a 10.1007/978-3-030-60365-6 $2 doi
035 $a 978-3-030-60365-6
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis $h [electronic resource] : $b Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings / $c edited by Carole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, Sarah Parisot.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XVII, 222 p. 85 illus., 76 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 12443
505 0 $a UNSURE 2020 -- Image registration via stochastic gradient Markov chain Monte Carlo -- RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification -- Hierarchical brain parcellation with uncertainty -- Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-Class Segmentation -- Uncertainty Estimation in Landmark Localization based on Gaussian Heatmaps -- Weight averaging impact on the uncertainty of retinal artery-venous segmentation -- Improving Pathological Distribution Measurements with Bayesian Uncertainty -- Improving Reliability of Clinical Models using Prediction Calibration -- Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior -- Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability -- GRAIL 2020 -- Clustering-based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates -- Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences -- Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders -- Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Proling -- Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation -- Min-cut Max-flow for Network Abnormality Detection: Application to Preterm Birth -- Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface -- The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification -- Intraoperative Liver Surface Completion with Graph Convolutional VAE -- HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification.
520 $a This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 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. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
650 0 $a Artificial intelligence. $3 559380
650 0 $a Pattern recognition. $3 1253525
650 0 $a Optical data processing. $3 639187
650 0 $a Application software. $3 528147
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 669920
700 1 $a Sudre, Carole H. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300250
700 1 $a Fehri, Hamid. $e editor. $1 https://orcid.org/0000-0003-2017-2670 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327009
700 1 $a Arbel, Tal. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1200908
700 1 $a Baumgartner, Christian F. $e editor. $1 https://orcid.org/0000-0002-3629-4384 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327010
700 1 $a Dalca, Adrian. $e editor. $1 https://orcid.org/0000-0002-8422-0136 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300249
700 1 $a Tanno, Ryutaro. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300248
700 1 $a Van Leemput, Koen. $e editor. $1 https://orcid.org/0000-0001-6466-5309 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327011
700 1 $a Wells, William M. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1254654
700 1 $a Sotiras, Aristeidis. $e editor. $1 https://orcid.org/0000-0003-0795-8820 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1278883
700 1 $a Papiez, Bartlomiej. $e editor. $1 https://orcid.org/0000-0002-8432-2511 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1278884
700 1 $a Ferrante, Enzo. $e editor. $1 https://orcid.org/0000-0002-8500-788X $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1278879
700 1 $a Parisot, Sarah. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1278882
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030603649
776 0 8 $i Printed edition: $z 9783030603663
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-030-60365-6
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)