Please use this identifier to cite or link to this item:
http://localhost/handle/Hannan/349
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Meller, Henning. ; | en_US |
dc.contributor.author | Kelm, B. Michael. ; | en_US |
dc.contributor.author | Arbel, Tal. ; | en_US |
dc.contributor.author | Cai, Weidong. ; | en_US |
dc.contributor.author | Cardoso, M. Jorge. ; | en_US |
dc.contributor.author | Langs, Georg. ; | en_US |
dc.contributor.author | Menze, Bjoern. ; | en_US |
dc.contributor.author | Metaxas, Dimitris. ; | en_US |
dc.contributor.author | Montillo, Albert. ; | en_US |
dc.contributor.author | Wells III, William M. ; | en_US |
dc.contributor.author | Zhang, Shaoting. ; | en_US |
dc.contributor.author | Chung, Albert C.S. ; | en_US |
dc.contributor.author | Jenkinson, Mark | en_US |
dc.date.accessioned | 2013 | en_US |
dc.date.accessioned | 2020-04-28T08:52:37Z | - |
dc.date.available | 2020-04-28T08:52:37Z | - |
dc.date.issued | 2017 | en_US |
dc.identifier.isbn | 9783319611884 ; | en_US |
dc.identifier.isbn | 9783319611877 (print) ; | en_US |
dc.identifier.uri | http://localhost/handle/Hannan/349 | - |
dc.description | SpringerLink (Online service) ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description | Printed edition: ; 9783319611877. ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description.abstract | This 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.statementofresponsibility | edited 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.tableofcontents | Constructing 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.extent | XIII, 222 p. 75 illus. ; online resource. ; | en_US |
dc.publisher | Springer International Publishing : | en_US |
dc.publisher | Imprint: Springer, | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ; | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ; | en_US |
dc.relation.haspart | 9783319611884.pdf | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Health informatics. ; | en_US |
dc.subject | Mathematical statistics. ; | en_US |
dc.subject | Computer Science and Mathematics | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Image processing. ; | en_US |
dc.subject | Pattern recognition. ; | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Image Processing and Computer Vision. ; | en_US |
dc.subject | Health Informatics. ; | en_US |
dc.subject | Artificial Intelligence and Robotics | en_US |
dc.subject | Probability and Statistics in Computer Science. ; | en_US |
dc.subject | Math Applications in Computer Science. ; | en_US |
dc.subject | Pattern Recognition. ; | en_US |
dc.title | Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging | en_US |
dc.title.alternative | MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers / | en_US |
dc.type | Book | en_US |
dc.publisher.place | Cham : | en_US |
dc.classification.lc | TA1634 ; | en_US |
dc.classification.dc | 006.6 ; 23 ; | en_US |
dc.classification.dc | 006.37 ; 23 ; | en_US |
Appears in Collections: | مهندسی مدیریت ساخت |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
9783319611884.pdf | 33.36 MB | Adobe PDF | Preview File |
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Meller, Henning. ; | en_US |
dc.contributor.author | Kelm, B. Michael. ; | en_US |
dc.contributor.author | Arbel, Tal. ; | en_US |
dc.contributor.author | Cai, Weidong. ; | en_US |
dc.contributor.author | Cardoso, M. Jorge. ; | en_US |
dc.contributor.author | Langs, Georg. ; | en_US |
dc.contributor.author | Menze, Bjoern. ; | en_US |
dc.contributor.author | Metaxas, Dimitris. ; | en_US |
dc.contributor.author | Montillo, Albert. ; | en_US |
dc.contributor.author | Wells III, William M. ; | en_US |
dc.contributor.author | Zhang, Shaoting. ; | en_US |
dc.contributor.author | Chung, Albert C.S. ; | en_US |
dc.contributor.author | Jenkinson, Mark | en_US |
dc.date.accessioned | 2013 | en_US |
dc.date.accessioned | 2020-04-28T08:52:37Z | - |
dc.date.available | 2020-04-28T08:52:37Z | - |
dc.date.issued | 2017 | en_US |
dc.identifier.isbn | 9783319611884 ; | en_US |
dc.identifier.isbn | 9783319611877 (print) ; | en_US |
dc.identifier.uri | http://localhost/handle/Hannan/349 | - |
dc.description | SpringerLink (Online service) ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description | Printed edition: ; 9783319611877. ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description.abstract | This 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.statementofresponsibility | edited 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.tableofcontents | Constructing 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.extent | XIII, 222 p. 75 illus. ; online resource. ; | en_US |
dc.publisher | Springer International Publishing : | en_US |
dc.publisher | Imprint: Springer, | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ; | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ; | en_US |
dc.relation.haspart | 9783319611884.pdf | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Health informatics. ; | en_US |
dc.subject | Mathematical statistics. ; | en_US |
dc.subject | Computer Science and Mathematics | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Image processing. ; | en_US |
dc.subject | Pattern recognition. ; | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Image Processing and Computer Vision. ; | en_US |
dc.subject | Health Informatics. ; | en_US |
dc.subject | Artificial Intelligence and Robotics | en_US |
dc.subject | Probability and Statistics in Computer Science. ; | en_US |
dc.subject | Math Applications in Computer Science. ; | en_US |
dc.subject | Pattern Recognition. ; | en_US |
dc.title | Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging | en_US |
dc.title.alternative | MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers / | en_US |
dc.type | Book | en_US |
dc.publisher.place | Cham : | en_US |
dc.classification.lc | TA1634 ; | en_US |
dc.classification.dc | 006.6 ; 23 ; | en_US |
dc.classification.dc | 006.37 ; 23 ; | en_US |
Appears in Collections: | مهندسی مدیریت ساخت |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
9783319611884.pdf | 33.36 MB | Adobe PDF | Preview File |
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Meller, Henning. ; | en_US |
dc.contributor.author | Kelm, B. Michael. ; | en_US |
dc.contributor.author | Arbel, Tal. ; | en_US |
dc.contributor.author | Cai, Weidong. ; | en_US |
dc.contributor.author | Cardoso, M. Jorge. ; | en_US |
dc.contributor.author | Langs, Georg. ; | en_US |
dc.contributor.author | Menze, Bjoern. ; | en_US |
dc.contributor.author | Metaxas, Dimitris. ; | en_US |
dc.contributor.author | Montillo, Albert. ; | en_US |
dc.contributor.author | Wells III, William M. ; | en_US |
dc.contributor.author | Zhang, Shaoting. ; | en_US |
dc.contributor.author | Chung, Albert C.S. ; | en_US |
dc.contributor.author | Jenkinson, Mark | en_US |
dc.date.accessioned | 2013 | en_US |
dc.date.accessioned | 2020-04-28T08:52:37Z | - |
dc.date.available | 2020-04-28T08:52:37Z | - |
dc.date.issued | 2017 | en_US |
dc.identifier.isbn | 9783319611884 ; | en_US |
dc.identifier.isbn | 9783319611877 (print) ; | en_US |
dc.identifier.uri | http://localhost/handle/Hannan/349 | - |
dc.description | SpringerLink (Online service) ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description | Printed edition: ; 9783319611877. ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description.abstract | This 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.statementofresponsibility | edited 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.tableofcontents | Constructing 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.extent | XIII, 222 p. 75 illus. ; online resource. ; | en_US |
dc.publisher | Springer International Publishing : | en_US |
dc.publisher | Imprint: Springer, | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ; | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science, ; 0302-9743 ; ; 10081. ; | en_US |
dc.relation.haspart | 9783319611884.pdf | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Health informatics. ; | en_US |
dc.subject | Mathematical statistics. ; | en_US |
dc.subject | Computer Science and Mathematics | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Image processing. ; | en_US |
dc.subject | Pattern recognition. ; | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Image Processing and Computer Vision. ; | en_US |
dc.subject | Health Informatics. ; | en_US |
dc.subject | Artificial Intelligence and Robotics | en_US |
dc.subject | Probability and Statistics in Computer Science. ; | en_US |
dc.subject | Math Applications in Computer Science. ; | en_US |
dc.subject | Pattern Recognition. ; | en_US |
dc.title | Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging | en_US |
dc.title.alternative | MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers / | en_US |
dc.type | Book | en_US |
dc.publisher.place | Cham : | en_US |
dc.classification.lc | TA1634 ; | en_US |
dc.classification.dc | 006.6 ; 23 ; | en_US |
dc.classification.dc | 006.37 ; 23 ; | en_US |
Appears in Collections: | مهندسی مدیریت ساخت |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
9783319611884.pdf | 33.36 MB | Adobe PDF | Preview File |