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000945196 019__ $$a1199127403$$a1204061983$$a1224538684
000945196 020__ $$a9783030603656$$q(electronic book)
000945196 020__ $$a3030603652$$q(electronic book)
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000945196 020__ $$z9783030603649
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000945196 08204 $$a616.07/54$$223
000945196 1112_ $$aUNSURE (Workshop)$$n(2nd :$$d2020 :$$cOnline)
000945196 24510 $$aUncertainty for safe utilization of machine learning in medical imaging, and graphs in biomedical image analysis :$$bsecond International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /$$cCarole H. Sudre, Hamid Fehri et al. (eds.).
000945196 2463_ $$aUNSURE 2020
000945196 2463_ $$aGRAIL 2020
000945196 264_1 $$aCham :$$bSpringer,$$c2020.
000945196 300__ $$a1 online resource (XVII, 222 pages) :$$billustrations.
000945196 336__ $$atext$$btxt$$2rdacontent
000945196 337__ $$acomputer$$bc$$2rdamedia
000945196 338__ $$aonline resource$$bcr$$2rdacarrier
000945196 347__ $$atext file$$bPDF$$2rda
000945196 4901_ $$aLecture notes in computer science ;$$v12443
000945196 4901_ $$aLNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics
000945196 500__ $$a"Organized as a satellite event of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020) in Lima, Peru, which was held completely virtually due to the COVID-19 pandemic."--Preface
000945196 500__ $$aInternational conference proceedings.
000945196 500__ $$aIncludes author index.
000945196 5050_ $$aUNSURE 2020 -- Image registration via stochastic gradient Markov chain Monte Carlo -- RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification -- Hierarchical brain parcellation with uncertainty -- Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-Class Segmentation -- Uncertainty Estimation in Landmark Localization based on Gaussian Heatmaps -- Weight averaging impact on the uncertainty of retinal artery-venous segmentation -- Improving Pathological Distribution Measurements with Bayesian Uncertainty -- Improving Reliability of Clinical Models using Prediction Calibration -- Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior -- Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability -- GRAIL 2020 -- Clustering-based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates -- Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences -- Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders -- Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Proling -- Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation -- Min-cut Max-flow for Network Abnormality Detection: Application to Preterm Birth -- Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface -- The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification -- Intraoperative Liver Surface Completion with Graph Convolutional VAE -- HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification.
000945196 506__ $$aAccess limited to authorized users.
000945196 520__ $$aThis book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
000945196 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 14, 2020).
000945196 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
000945196 650_0 $$aArtificial intelligence$$xMedical applications$$vCongresses.
000945196 650_0 $$aMachine learning$$vCongresses.
000945196 7001_ $$aSudre, Carole H.,$$eeditor.
000945196 7001_ $$aFehri, Hamid.,$$eeditor.
000945196 7001_ $$aArbel, Tal,$$eeditor.
000945196 7001_ $$aBaumgartner, Christian F.,$$eeditor.
000945196 7001_ $$aDalca, Adrian.,$$eeditor.
000945196 7001_ $$aTanno, Ryutaro.,$$eeditor.
000945196 7001_ $$aVan Leemput, Koen.,$$eeditor.
000945196 7001_ $$aWells, William M.,$$eeditor.
000945196 7001_ $$aSotiras, Aristeidis.,$$eeditor.
000945196 7001_ $$aPapiez, Bartlomiej.,$$eeditor.
000945196 7001_ $$aFerrante, Enzo.,$$eeditor.
000945196 7001_ $$aParisot, Sarah.,$$eeditor.
000945196 7112_ $$aGRAIL (Workshop)$$n(3rd :$$d2020 :$$cOnline)
000945196 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(23rd :$$d2020 :$$cLima, Peru)
000945196 77608 $$iPrint version:$$aSudre, Carole H.$$tUncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis : Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings.$$dCham : Springer International Publishing AG, ©2020$$z9783030603649
000945196 830_0 $$aLecture notes in computer science ;$$v12443.
000945196 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
000945196 852__ $$bebk
000945196 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-60365-6$$zOnline Access$$91397441.1
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000945196 980__ $$aEBOOK
000945196 980__ $$aBIB
000945196 982__ $$aEbook
000945196 983__ $$aOnline
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