LEADER 06784nam 22007935 450 001 996466031503316 005 20200701061503.0 010 $a3-319-02267-9 024 7 $a10.1007/978-3-319-02267-3 035 $a(CKB)3710000000019109 035 $a(SSID)ssj0001010875 035 $a(PQKBManifestationID)11556863 035 $a(PQKBTitleCode)TC0001010875 035 $a(PQKBWorkID)11003323 035 $a(PQKB)11258645 035 $a(DE-He213)978-3-319-02267-3 035 $a(MiAaPQ)EBC3107067 035 $a(PPN)172424593 035 $a(EXLCZ)993710000000019109 100 $a20130918d2013 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning in Medical Imaging$b[electronic resource] $e4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings /$fedited by Guorong Wu, Daoqiang Zhang, Dinggang Shen, Pingkun Yan, Kenji Suzuki, Fei Wang 205 $a1st ed. 2013. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2013. 215 $a1 online resource (XII, 262 p. 94 illus.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v8184 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-02266-0 327 $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: A Comparative 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. 330 $aThis book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, in Nagoya, Japan, in September 2013. The 32 contributions included in this volume were carefully reviewed and selected from 57 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v8184 606 $aOptical data processing 606 $aPattern recognition 606 $aArtificial intelligence 606 $aDatabase management 606 $aComputer graphics 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aArtificial intelligence. 615 0$aDatabase management. 615 0$aComputer graphics. 615 14$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aArtificial Intelligence. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aDatabase Management. 615 24$aComputer Graphics. 676 $a006.6 676 $a006.37 702 $aWu$b Guorong$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhang$b Daoqiang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aShen$b Dinggang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYan$b Pingkun$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSuzuki$b Kenji$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWang$b Fei$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996466031503316 996 $aMachine Learning in Medical Imaging$91918862 997 $aUNISA LEADER 02839oas 22011173a 450 001 9910140518603321 005 20260203110344.0 011 $a1651-1948 035 $a(DE-599)ZDB2696155-6 035 $a(OCoLC)43074314 035 $a(CONSER) 2012219041 035 $a(CKB)963017820267 035 $a(DE-599)2070781-2 035 $a(EXLCZ)99963017820267 100 $a19991221b19992012 sy a 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in physiotherapy 210 1$a[Oslo, Norway] :$cScandinavian University Press,$d[1999-2012] 210 4$d©1999-©2012 210 31$a[London, UK] :$cTaylor & Francis 215 $a1 online resource 300 $aRefereed/Peer-reviewed 311 08$a1403-8196 531 0 $aAdv. physiother. 606 $aTherapeutics, Physiological$vPeriodicals 606 $aPhysical therapy$vPeriodicals 606 $aPhysical Therapy Modalities 606 $aTherapeutics 606 $aPhysiothe?rapie$vPe?riodiques 606 $aMe?decines paralle?les$vPe?riodiques 606 $aPhysical therapy$2fast$3(OCoLC)fst01062771 606 $aTherapeutics, Physiological$2fast$3(OCoLC)fst01149711 606 $aTerapčutica$2thub 606 $aTerapčutica fisiolņgica$2thub 606 $aFisioterąpia$2thub 608 $aPeriodical. 608 $aPeriodicals.$2fast 608 $aRevistes electrņniques.$2thub 615 0$aTherapeutics, Physiological 615 0$aPhysical therapy 615 2$aPhysical Therapy Modalities. 615 2$aTherapeutics. 615 6$aPhysiothe?rapie 615 6$aMe?decines paralle?les 615 7$aPhysical therapy. 615 7$aTherapeutics, Physiological. 615 7$aTerapčutica. 615 7$aTerapčutica fisiolņgica. 615 7$aFisioterąpia. 676 $a615.82 801 0$bF#A 801 1$bF#A 801 2$bOCLCQ 801 2$bVAM 801 2$bOCLCQ 801 2$bZ5A 801 2$bHUL 801 2$bDLC 801 2$bOCLCQ 801 2$bDLC 801 2$bUKMGB 801 2$bOCLCO 801 2$bCOO 801 2$bOCLCO 801 2$bCUI 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCF 801 2$bOCLCO 801 2$bOCLCQ 801 2$bTXA 801 2$bBUF 801 2$bEZC 801 2$bOCLCO 801 2$bAU@ 801 2$bOCLCO 801 2$bWYU 801 2$bOCLCO 801 2$bVT2 801 2$bU3W 801 2$bOCLCQ 801 2$bOCLCO 801 2$bBWN 801 2$bOCLCQ 801 2$bUAB 801 2$bSFB 801 2$bUEJ 801 2$bOCLCL 906 $aJOURNAL 912 $a9910140518603321 996 $aAdvances in physiotherapy$92565300 997 $aUNINA