DSpace Repository

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging [electronic resource] : MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers / edited by Henning M ller, 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.

Show simple item record

dc.contributor.author M ller, Henning. editor.
dc.contributor.author Kelm, B. Michael. editor.
dc.contributor.author Arbel, Tal. editor.
dc.contributor.author Cai, Weidong. editor.
dc.contributor.author Cardoso, M. Jorge. editor.
dc.contributor.author Langs, Georg. editor.
dc.contributor.author Menze, Bjoern. editor.
dc.contributor.author Metaxas, Dimitris. editor.
dc.contributor.author Montillo, Albert. editor.
dc.contributor.author Wells III, William M. editor.
dc.contributor.author Zhang, Shaoting. editor.
dc.contributor.author Chung, Albert C.S. editor.
dc.contributor.author Jenkinson, Mark. editor.
dc.contributor.author Ribbens, Annemie. editor.
dc.contributor.author SpringerLink (Online service)
dc.date.accessioned 2017-12-02T14:41:36Z
dc.date.available 2017-12-02T14:41:36Z
dc.date.created 2017.
dc.date.issued 2017
dc.identifier.isbn 9783319611884
dc.identifier.uri http://dspace.conacyt.gov.py/xmlui/handle/123456789/22095
dc.description XIII, 222 p. 75 illus.
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 data” 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.
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. .
dc.language eng
dc.publisher Cham : Springer International Publishing : Imprint: Springer, 2017.
dc.relation.ispartofseries Springer eBooks
dc.relation.ispartofseries Lecture Notes in Computer Science, 0302-9743 ; 10081
dc.relation.ispartofseries Lecture Notes in Computer Science, 0302-9743 ; 10081
dc.relation.uri http://cicco.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-61188-4
dc.subject Computer science.
dc.subject Health informatics.
dc.subject Mathematical statistics.
dc.subject Computer science Mathematics.
dc.subject Artificial intelligence.
dc.subject Image processing.
dc.subject Pattern recognition.
dc.subject Computer Science.
dc.subject Image Processing and Computer Vision.
dc.subject Health Informatics.
dc.subject Artificial Intelligence (incl. Robotics).
dc.subject Probability and Statistics in Computer Science.
dc.subject Math Applications in Computer Science.
dc.subject Pattern Recognition.
dc.subject.ddc 006.6 23
dc.subject.ddc 006.37 23
dc.subject.lcc TA1637-1638
dc.subject.lcc TA1634
dc.subject.other Computer Science (Springer-11645)
dc.title Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging [electronic resource] : MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers / edited by Henning M ller, 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.
dc.type text
dc.identifier.doi 10.1007/978-3-319-61188-4
dc.identifier.bib 978-3-319-61188-4
dc.format.rdamedia computer
dc.format.rdacarrier online resource
dc.format.rda text file PDF


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account