TY  - BOOK
AU  - Müller,Henning
AU  - Kelm,B.Michael
AU  - Arbel,Tal
AU  - Cai,Weidong
AU  - Cardoso,M.Jorge
AU  - Langs,Georg
AU  - Menze,Bjoern
AU  - Metaxas,Dimitris N.
AU  - Montillo,Albert
AU  - Wells,William M.
AU  - Zhang,Shaoting
AU  - Chung,Albert C.S.
AU  - Jenkinson,Mark
AU  - Ribbens,Annemie
ED  - MCV (Workshop)
ED  - BAMBI (Workshop)
ED  - International Conference on Medical Image Computing and Computer-Assisted Intervention
TI  - Medical computer vision and Bayesian and graphical models for biomedical imaging: MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised selected papers 
T2  - Lecture notes in computer science,
SN  - 9783319611884
AV  - R859.7.C67 
U1  - 610.2856 23
PY  - 2017///
CY  - Cham, Switzerland
PB  - Springer
KW  - Computer vision in medicine
KW  - Congresses
KW  - Diagnostic imaging
KW  - Data processing
KW  - Therapy, Computer-Assisted
KW  - Vision par ordinateur en médecine
KW  - Congrès
KW  - Imagerie pour le diagnostic
KW  - Informatique
KW  - Health & safety aspects of IT
KW  - bicssc
KW  - Artificial intelligence
KW  - Maths for computer scientists
KW  - Pattern recognition
KW  - Image processing
KW  - Medical
KW  - General
KW  - bisacsh
KW  - Computers
KW  - Intelligence (AI) & Semantics
KW  - Mathematical & Statistical Software
KW  - Data Processing
KW  - Computer Vision & Pattern Recognition
KW  - Computer Graphics
KW  - fast
KW  - Congress
KW  - proceedings (reports)
KW  - aat
KW  - Conference papers and proceedings
KW  - lcgft
KW  - Actes de congrès
KW  - rvmgf
N1  - Includes author index; Includes bibliographical references and author index; 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
N2  - 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
UR  - https://link.springer.com/10.1007/978-3-319-61188-4
ER  -