Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/349
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMeller, Henning. ;en_US
dc.contributor.authorKelm, B. Michael. ;en_US
dc.contributor.authorArbel, Tal. ;en_US
dc.contributor.authorCai, Weidong. ;en_US
dc.contributor.authorCardoso, M. Jorge. ;en_US
dc.contributor.authorLangs, Georg. ;en_US
dc.contributor.authorMenze, Bjoern. ;en_US
dc.contributor.authorMetaxas, Dimitris. ;en_US
dc.contributor.authorMontillo, Albert. ;en_US
dc.contributor.authorWells III, William M. ;en_US
dc.contributor.authorZhang, Shaoting. ;en_US
dc.contributor.authorChung, Albert C.S. ;en_US
dc.contributor.authorJenkinson, Marken_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-28T08:52:37Z-
dc.date.available2020-04-28T08:52:37Z-
dc.date.issued2017en_US
dc.identifier.isbn9783319611884 ;en_US
dc.identifier.isbn9783319611877 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/349-
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319611877. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big dataee algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis. ;en_US
dc.description.statementofresponsibilityedited by Henning Meller, B. Michael Kelm, Tal Arbel, Weidong Cai, M. Jorge Cardoso, Georg Langs, Bjoern Menze, Dimitris Metaxas, Albert Montillo, William M. Wells III, Shaoting Zhang, Albert C.S. Chung, Mark Jenkinson, Annemie Ribbens.en_US
dc.description.tableofcontentsConstructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases -- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases -- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images -- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images -- Inferring Disease Status by non-Parametric Probabilistic Embedding -- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images -- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study -- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker -- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation -- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images -- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features -- Representation Learning for Cross-Modality Classification -- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound -- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images -- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data -- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields -- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data -- Non-local Graph-based Regularization for Deformable Image Registration -- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. . ;en_US
dc.format.extentXIII, 222 p. 75 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ;en_US
dc.relation.haspart9783319611884.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectHealth informatics. ;en_US
dc.subjectMathematical statistics. ;en_US
dc.subjectComputer Science and Mathematicsen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectImage processing. ;en_US
dc.subjectPattern recognition. ;en_US
dc.subjectComputer Scienceen_US
dc.subjectImage Processing and Computer Vision. ;en_US
dc.subjectHealth Informatics. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.subjectProbability and Statistics in Computer Science. ;en_US
dc.subjectMath Applications in Computer Science. ;en_US
dc.subjectPattern Recognition. ;en_US
dc.titleMedical Computer Vision and Bayesian and Graphical Models for Biomedical Imagingen_US
dc.title.alternativeMICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcTA1634 ;en_US
dc.classification.dc006.6 ; 23 ;en_US
dc.classification.dc006.37 ; 23 ;en_US
Appears in Collections:مهندسی مدیریت ساخت

Files in This Item:
File Description SizeFormat 
9783319611884.pdf33.36 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMeller, Henning. ;en_US
dc.contributor.authorKelm, B. Michael. ;en_US
dc.contributor.authorArbel, Tal. ;en_US
dc.contributor.authorCai, Weidong. ;en_US
dc.contributor.authorCardoso, M. Jorge. ;en_US
dc.contributor.authorLangs, Georg. ;en_US
dc.contributor.authorMenze, Bjoern. ;en_US
dc.contributor.authorMetaxas, Dimitris. ;en_US
dc.contributor.authorMontillo, Albert. ;en_US
dc.contributor.authorWells III, William M. ;en_US
dc.contributor.authorZhang, Shaoting. ;en_US
dc.contributor.authorChung, Albert C.S. ;en_US
dc.contributor.authorJenkinson, Marken_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-28T08:52:37Z-
dc.date.available2020-04-28T08:52:37Z-
dc.date.issued2017en_US
dc.identifier.isbn9783319611884 ;en_US
dc.identifier.isbn9783319611877 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/349-
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319611877. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big dataee algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis. ;en_US
dc.description.statementofresponsibilityedited by Henning Meller, B. Michael Kelm, Tal Arbel, Weidong Cai, M. Jorge Cardoso, Georg Langs, Bjoern Menze, Dimitris Metaxas, Albert Montillo, William M. Wells III, Shaoting Zhang, Albert C.S. Chung, Mark Jenkinson, Annemie Ribbens.en_US
dc.description.tableofcontentsConstructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases -- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases -- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images -- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images -- Inferring Disease Status by non-Parametric Probabilistic Embedding -- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images -- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study -- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker -- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation -- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images -- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features -- Representation Learning for Cross-Modality Classification -- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound -- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images -- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data -- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields -- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data -- Non-local Graph-based Regularization for Deformable Image Registration -- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. . ;en_US
dc.format.extentXIII, 222 p. 75 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ;en_US
dc.relation.haspart9783319611884.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectHealth informatics. ;en_US
dc.subjectMathematical statistics. ;en_US
dc.subjectComputer Science and Mathematicsen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectImage processing. ;en_US
dc.subjectPattern recognition. ;en_US
dc.subjectComputer Scienceen_US
dc.subjectImage Processing and Computer Vision. ;en_US
dc.subjectHealth Informatics. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.subjectProbability and Statistics in Computer Science. ;en_US
dc.subjectMath Applications in Computer Science. ;en_US
dc.subjectPattern Recognition. ;en_US
dc.titleMedical Computer Vision and Bayesian and Graphical Models for Biomedical Imagingen_US
dc.title.alternativeMICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcTA1634 ;en_US
dc.classification.dc006.6 ; 23 ;en_US
dc.classification.dc006.37 ; 23 ;en_US
Appears in Collections:مهندسی مدیریت ساخت

Files in This Item:
File Description SizeFormat 
9783319611884.pdf33.36 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMeller, Henning. ;en_US
dc.contributor.authorKelm, B. Michael. ;en_US
dc.contributor.authorArbel, Tal. ;en_US
dc.contributor.authorCai, Weidong. ;en_US
dc.contributor.authorCardoso, M. Jorge. ;en_US
dc.contributor.authorLangs, Georg. ;en_US
dc.contributor.authorMenze, Bjoern. ;en_US
dc.contributor.authorMetaxas, Dimitris. ;en_US
dc.contributor.authorMontillo, Albert. ;en_US
dc.contributor.authorWells III, William M. ;en_US
dc.contributor.authorZhang, Shaoting. ;en_US
dc.contributor.authorChung, Albert C.S. ;en_US
dc.contributor.authorJenkinson, Marken_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-28T08:52:37Z-
dc.date.available2020-04-28T08:52:37Z-
dc.date.issued2017en_US
dc.identifier.isbn9783319611884 ;en_US
dc.identifier.isbn9783319611877 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/349-
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319611877. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big dataee algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis. ;en_US
dc.description.statementofresponsibilityedited by Henning Meller, B. Michael Kelm, Tal Arbel, Weidong Cai, M. Jorge Cardoso, Georg Langs, Bjoern Menze, Dimitris Metaxas, Albert Montillo, William M. Wells III, Shaoting Zhang, Albert C.S. Chung, Mark Jenkinson, Annemie Ribbens.en_US
dc.description.tableofcontentsConstructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases -- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases -- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images -- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images -- Inferring Disease Status by non-Parametric Probabilistic Embedding -- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images -- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study -- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker -- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation -- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images -- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features -- Representation Learning for Cross-Modality Classification -- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound -- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images -- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data -- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields -- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data -- Non-local Graph-based Regularization for Deformable Image Registration -- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. . ;en_US
dc.format.extentXIII, 222 p. 75 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ;en_US
dc.relation.haspart9783319611884.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectHealth informatics. ;en_US
dc.subjectMathematical statistics. ;en_US
dc.subjectComputer Science and Mathematicsen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectImage processing. ;en_US
dc.subjectPattern recognition. ;en_US
dc.subjectComputer Scienceen_US
dc.subjectImage Processing and Computer Vision. ;en_US
dc.subjectHealth Informatics. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.subjectProbability and Statistics in Computer Science. ;en_US
dc.subjectMath Applications in Computer Science. ;en_US
dc.subjectPattern Recognition. ;en_US
dc.titleMedical Computer Vision and Bayesian and Graphical Models for Biomedical Imagingen_US
dc.title.alternativeMICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcTA1634 ;en_US
dc.classification.dc006.6 ; 23 ;en_US
dc.classification.dc006.37 ; 23 ;en_US
Appears in Collections:مهندسی مدیریت ساخت

Files in This Item:
File Description SizeFormat 
9783319611884.pdf33.36 MBAdobe PDFThumbnail
Preview File