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Medical Image Computing and Computer Assisted Intervention - MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part IV - Softcover
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Inhaltsangabe
Shape.- A CNN-Based Framework for Statistical Assessment of Spinal Shape and Curvature in Whole-Body MRI Images of Large Populations.- Exploiting Reliability-guided Aggregation for the Assessment of Curvilinear Structure Tortuosity.- A Surface-theoretic Approach for Statistical Shape Modeling.- Shape Instantiation from A Single 2D Image to 3D Point Cloud with One-stage Learning.- Placental Flattening via Volumetric Parameterization with Dirichlet Energy Regularization.- Fast Polynomial Approximation to Heat Diffusion in Manifolds.- Hierarchical Multi-Geodesic Model for Longitudinal Analysis of Temporal Trajectories of Anatomical Shape and Covariates.- Clustering of longitudinal shape data sets using mixture of separate or branching trajectories.- Group-wise Graph Matching of Cortical Gyral Hinges.- Multi-view Graph Matching of Cortical Landmarks.- Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators.- Surface-Based Spatial Pyramid Matching of Cortical Regions for Analysis of Cognitive Performance.- Prediction.- Diagnosis-guided multi-modal feature selection for prognosis prediction of lung squamous cell carcinoma.- Graph convolution based attention model for personalized disease prediction.- Predicting Early Stages of Neurodegenerative Diseases via Multi-task Low-rank Feature Learning.- Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments Over Progressions.- Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction.- End-to-End Dementia Status Prediction from Brain MRI using Multi-Task Weakly-Supervised Attention Network.- Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression.- LSTM Network for Prediction of Hemorrhagic Transformation in Acute Stroke.- Inter-modality Dependence Induced Data Recovery for MCI Conversion Prediction.- Preprocessing, Prediction and Significance: Framework and Application to Brain Imaging.- Early Prediction of Alzheimer's Disease progression using Variational Autoencoder.- Integrating Heterogeneous Brain Networks for Predicting Brain Disease Conditions.- Detection and Localization.- Uncertainty-informed detection of epileptogenic brain malformations using Bayesian neural networks.- Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network.- Intracranial aneurysms detection in 3D cerebrovascular mesh model with ensemble deep learning.- Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks.- Multiple Landmarks Detection using Multi-Agent Reinforcement Learning.- Spatiotemporal Breast Mass Detection Network (MD-Net) in 4D DCE-MRI Images.- Automated Pulmonary Embolism Detection from CTPA Images using an End-to-End Convolutional Neural Network.- Pixel-wise anomaly ratings using Variational Auto-Encoders.- HR-CAM: Precise Localization of pathology using multi-level learning in CNNs.- Novel Iterative Attention Focusing Strategy for Joint Pathology Localization and Diagnosis of MCI Progression.- Automatic Vertebrae Recognition from Arbitrary Spine MRI images by a Hierarchical Self-calibration Detection Framework.- Machine Learning.- Image data validation for medical systems.- Captioning Ultrasound Images Automatically.- Feature Transformers: Privacy Preserving Life Learning Framework for Healthcare Applications.- As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging.- Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance with Application to Skin Lesion Classification.- Learning task-specific and shared representations in medical imaging.- Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis.- Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.- Fetal Pose Estimation in Volumetric MRI using 3D Convolution Neural Network.- Multi-Stage Pred
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