000 06218nam a22006735i 4500
001 978-3-319-02267-3
003 DE-He213
005 20240730202437.0
007 cr nn 008mamaa
008 130918s2013 sz | s |||| 0|eng d
020 _a9783319022673
_9978-3-319-02267-3
024 7 _a10.1007/978-3-319-02267-3
_2doi
050 4 _aTA1634
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQV
_2thema
082 0 4 _a006.37
_223
245 1 0 _aMachine Learning in Medical Imaging
_h[electronic resource] :
_b4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings /
_cedited by Guorong Wu, Daoqiang Zhang, Dinggang Shen, Pingkun Yan, Kenji Suzuki, Fei Wang.
250 _a1st ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXII, 262 p. 94 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v8184
505 0 _aUnsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR Images -- Integrating Multiple Network Properties for MCI Identification -- Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentation -- Volumetric Segmentation of Key Fetal Brain Structures in 3D Ultrasound -- Sparse Classification with MRI Based Markers for Neuromuscular Disease Categorization -- Fully Automatic Detection of the Carotid Artery from Volumetric Ultrasound Images Using Anatomical Position-Dependent LBP Features -- A Transfer-Learning Approach to Image Segmentation Across Scanners by Maximizing Distribution Similarity -- A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography -- A Unified Approach to Shape Model Fitting and Non-rigid Registration -- A Bayesian Algorithm for Image-Based Time-to-Event Prediction -- Patient-Specific Manifold Embedding of Multispectral Images Using Kernel Combinations -- fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics -- Patch-Based Segmentation without Registration: Application to Knee MRI -- Flow-Based Correspondence Matching in Stereovision -- Thickness NETwork (ThickNet) Features for the Detection of Prodromal AD -- Metric Space Structures for Computational Anatomy -- Discriminative Group Sparse Representation for Mild Cognitive Impairment Classification -- Temporally Dynamic Resting-State Functional Connectivity Networks for Early MCI Identification -- An Improved Optimization Method for the Relevance Voxel Machine -- Disentanglement of Session and Plasticity Effects in Longitudinal fMRI Studies -- Identification of Alzheimer's Disease Using Incomplete Multimodal Dataset via Matrix Shrinkage and Completion -- On Feature Relevance in Image-Based Prediction Models: An Empirical Study -- Decision Forests with Spatio-Temporal Features for Graph-Based Tumor Segmentation in 4D Lung CT -- Improving Probabilistic Image Registration via Reinforcement Learning and Uncertainty Evaluation -- HEp-2 Cell Image Classification: AComparative Analysis -- A 2.5D Colon Wall Flattening Model for CT-Based Virtual Colonoscopy -- Augmenting Auto-context with Global Geometric Features for Spinal Cord Segmentation -- Large-Scale Manifold Learning Using an Adaptive Sparse Neighbor Selection Approach for Brain Tumor Progression Prediction -- Ensemble Universum SVM Learning for Multimodal Classification of Alzheimer's Disease -- Joint Sparse Coding Spatial Pyramid Matching for Classification of Color Blood Cell Image -- Multi-task Sparse Classifier for Diagnosis of MCI Conversion to AD with Longitudinal MR Images -- Sparse Multimodal Manifold-Regularized Transfer Learning for MCI Conversion Prediction.
520 _aThis book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, in Nagoya, Japan, in September 2013. The 32 contributions included in this volume were carefully reviewed and selected from 57 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.
650 0 _aComputer vision.
_9171454
650 0 _aPattern recognition systems.
_93953
650 0 _aArtificial intelligence.
_93407
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aDatabase management.
_93157
650 0 _aComputer graphics.
_94088
650 1 4 _aComputer Vision.
_9171455
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aDatabase Management.
_93157
650 2 4 _aComputer Graphics.
_94088
700 1 _aWu, Guorong.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9171456
700 1 _aZhang, Daoqiang.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9171457
700 1 _aShen, Dinggang.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9171458
700 1 _aYan, Pingkun.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9171459
700 1 _aSuzuki, Kenji.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9171460
700 1 _aWang, Fei.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9171461
710 2 _aSpringerLink (Online service)
_9171462
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319022666
776 0 8 _iPrinted edition:
_z9783319022680
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v8184
_9171463
856 4 0 _uhttps://doi.org/10.1007/978-3-319-02267-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cELN
999 _c97018
_d97018