LEADER 01348nam0 22002891i 450 001 UON00082777 005 20241209030346.723 010 $a84-00-04833-4 100 $a20020107d1981 |0itac50 ba 101 $aspa 102 $aES 105 $a|||| 1|||| 200 1 $aActas del I Simposio Internacional de Mudejarismo$b15-19 settembre 1975]$fConsejo Superior de Investigaciones Cientificas. Diputacion Provincial de Teruel 210 $aMadrid$cConsejo Superior de Investigaciones Cientificas$d1981 215 $a542 p.$d23 cm 316 $adata inesatta$5IT-UONSI CONG*/049 606 $aMUDEJARES$3UONC021269$2FI 620 $aES$dMadrid$3UONL000218 676 $a946.02$cStoria della Spagna. 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Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer?s Disease -- Multi-Factorial Age Estimation from Skeletal and Dental MRI Volumes -- Automatic Classification of Proximal Femur Fractures Based on Attention Models -- Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation -- Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble -- STAR: Spatio-Temporal Architecture for Super-Resolution inLow-Dose CT Perfusion -- Classification of Alzheimer?s Disease by Cascaded Convolutional Neural Networks Using PET Images -- Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images -- Multi-Scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base -- Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-Status Dementia Diagnosis -- 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels -- Efficient Groupwise Registration for Brain MRI by Fast Initialization -- Sparse Multi-View Task-centralized Learning for ASD Diagnosis -- Inter-Subject Similarity Guided Brain Network Modelling for MCI Diagnosis -- Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data -- Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images -- Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity -- Gradient Boosted Trees for Corrective Learning -- Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis -- A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling -- Collage CNN for Renal Cell Carcinoma Detection from CT -- Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images -- Localizing Cardiac Structures in Fetal Heart Ultrasound Video -- Deformable Registration Through Learning of Context-Specific Metric Aggregation -- Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-learning Based Cascade Framework -- 3D U-net with Multi-Level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images -- Indecisive Trees for Classification and Prediction of Knee Osteoarthritis -- Whole Brain Segmentation and Labeling from CT using synthetic MR Images -- Structural Connectivity Guided SparseEffective Connectivity for MCI Identification -- Fusion of High-order and Low-order Effective Connectivity Networks for MCI Classification -- Novel Effective Connectivity Network Inference for MCI Identification -- Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network -- Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to ?Virtual? High-Dose CT Images -- Deep-Fext: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction -- Product Space Decompositions for Continuous Representations of Brain Connectivity -- Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks -- Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging -- Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. 330 $aThis book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v10541 606 $aComputer vision 606 $aSoftware engineering 606 $aMedical informatics 606 $aData mining 606 $aArtificial intelligence 606 $aComputer Vision 606 $aSoftware Engineering 606 $aHealth Informatics 606 $aData Mining and Knowledge Discovery 606 $aArtificial Intelligence 615 0$aComputer vision. 615 0$aSoftware engineering. 615 0$aMedical informatics. 615 0$aData mining. 615 0$aArtificial intelligence. 615 14$aComputer Vision. 615 24$aSoftware Engineering. 615 24$aHealth Informatics. 615 24$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 676 $a006.31 702 $aWang$b Qian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aShi$b Yinghuan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSuk$b Heung-Il$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSuzuki$b Kenji$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484379503321 996 $aMachine Learning in Medical Imaging$92998079 997 $aUNINA