Machine Learning in Medical Imaging [electronic resource] :4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings /
Contributor(s): Wu, Guorong [editor.] | Zhang, Daoqiang [editor.] | Shen, Dinggang [editor.] | Yan, Pingkun [editor.] | Suzuki, Kenji [editor.] | Wang, Fei [editor.] | SpringerLink (Online service).Material type: BookSeries: Lecture Notes in Computer Science: 8184Publisher: Cham : Springer International Publishing : Imprint: Springer, 2013.Description: XII, 262 p. 94 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319022673.Subject(s): Computer science | Database management | Artificial intelligence | Computer graphics | Image processing | Pattern recognition | Computer Science | Image Processing and Computer Vision | Pattern Recognition | Artificial Intelligence (incl. Robotics) | Computer Imaging, Vision, Pattern Recognition and Graphics | Database Management | Computer GraphicsOnline resources: Click here to access online
Unsupervised 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.
This 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.