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Author:   Qian Wang ,  Yinghuan Shi ,  Heung-Il Suk ,  Kenji Suzuki
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2017
Volume:   10541
Weight:   6.146kg
ISBN:  

9783319673882

ISBN 10:   3319673882
Pages:   391
Publication Date:   07 September 2017
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

From Large to Small Organ Segmentation in CT Using Regional Context.- Motion Corruption Detection in Breast DCE-MRI.- Detection and Localization of Drosophila Egg Chambers in Microscopy Images.- Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-specific Coronary Calcium Scoring.- Atlas of Classifiers for Brain MRI Segmentation.- Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis.- Yet Another ADNI Machine Learning Paper? 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.

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