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Medical Computer Vision and Bayesian...

(1998 :)

Medical Computer Vision and Bayesian and Graphical Models for Biomedical ImagingMICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imagingedited by Henning Muller ... [et al.].
其他題名:	MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers /
其他題名:	MICCAI 2016
其他作者:	Muller, Henning.
團體作者:
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xiii, 222 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Computer vision in medicineCongresses.
電子資源:	http://dx.doi.org/10.1007/978-3-319-61188-4
ISBN:	9783319611884$q(electronic bk.)

Medical Computer Vision and Bayesian and Graphical Models for Biomedical ImagingMICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers /
Medical Computer Vision and Bayesian and Graphical Models for Biomedical ImagingMICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers /[electronic resource] :MICCAI 2016edited by Henning Muller ... [et al.]. - Cham :Springer International Publishing :2017. - xiii, 222 p. :ill., digital ;24 cm. - Lecture notes in computer science,100810302-9743 ;. - Lecture notes in computer science ;4891..

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.

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.

ISBN: 9783319611884$q(electronic bk.)

Standard No.: 10.1007/978-3-319-61188-4doiSubjects--Topical Terms:

445766
Computer vision in medicine
--Congresses.

LC Class. No.: R859.7.C67

Dewey Class. No.: 610.2856

Medical Computer Vision and Bayesian and Graphical Models for Biomedical ImagingMICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers /
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520 $a 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.
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