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Information Processing in Medical Imaging [[electronic resource] ] : 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings / / edited by Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao
| Information Processing in Medical Imaging [[electronic resource] ] : 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings / / edited by Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIX, 884 p. 517 illus., 331 illus. in color.) |
| Disciplina |
006.6
006.37 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Computer science—Mathematics Health informatics Computers Operating systems (Computers) Image Processing and Computer Vision Artificial Intelligence Mathematics of Computing Health Informatics Models and Principles Operating Systems |
| ISBN | 3-030-20351-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Segmentation -- A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration -- Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology -- Semi-Supervised and Task-Driven Data Augmentation -- Classification and Inference -- Analyzing Brain Morphology on the Bag-of-Features Manifold -- Modeling and Inference of Spatio-Temporal Protein Dynamics Across Brain Networks -- Deep Learning -- InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction -- Adaptive Graph Convolution Pooling for Brain Surface Analysis -- On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging -- A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging -- Improved Disease Classification in Chest X-rays with Transferred Features from Report Generation -- Reconstruction -- Limited Angle Tomography Reconstruction: Synthetic Reconstruction via Unsupervised Sinogram Adaptation -- Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences -- Disease Modeling -- Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia -- Shape -- Minimizing Non-Holonomicity: Finding Sheets in Fibrous Structures -- Learning Low-Dimensional Representations of Shape Data Sets with Diffeomorphic Autoencoders -- Diffeomorphic Medial Modeling -- Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing -- Registration -- Local Optimal Transport for Functional Brain Template Estimation -- Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations -- Learning Motion -- Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting -- Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces -- Functional Imaging -- Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG -- A Novel Sparse Overlapping Modularized Gaussian Graphical Model for Functional Connectivity Estimation -- White Matter Imaging -- Asymmetry Spectrum Imaging for Baby Diffusion Tractography -- A Fast Fiber k-Nearest-Neighbor Algorithm with Application to Group-Wise White Matter Topography Analysis -- Posters -- 3D Organ Shape Reconstruction from Topogram Images -- A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation -- A Graph Model of the Lungs with MorphologyBased Structure for Tuberculosis Type Classification -- A Longitudinal Model for Tau Aggregation in Alzheimers Disease Based on Structural Connectivity -- Accurate Nuclear Segmentation with Center Vector Encoding -- Bayesian Longitudinal Modeling of Early Stage Parkinsons Disease Using DaTscan Images -- Brain Tumor Segmentation on MRI with Missing Modalities -- Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation -- DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction -- ECKO: Ensemble of Clustered Knockoffs for Robust Multivariate Inference on fMRI Data -- FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms -- Graph Convolutional Nets for Tool Presence Detection in Surgical Videos -- High-Order Oriented Cylindrical Flux for Curvilinear Structure Detection and Vessel Segmentation -- Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network -- Learning a Conditional Generative Model for Anatomical Shape Analysis -- Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness -- Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data -- Riemannian Geometry Learning for Disease Progression Modelling -- Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model -- Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices with Applications to Neuroimaging -- Simultaneous Spatial-temporal Decomposition of Connectome-Scale Brain Networks by Deep Sparse Recurrent Auto-Encoders -- Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention -- A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces -- A Geometric Framework for Feature Mappings in Multimodal Fusion of Brain Image Data -- A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data -- A Model for Elastic Evolution on Foliated Shapes -- Analyzing Mild Cognitive Impairment Progression via Multi-view Structural Learning -- New Graph-Blind Convolutional Network for Brain Connectome Data Analysis -- CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation -- Data-Driven Model Order Reduction For Diffeomorphic Image Registration -- DGR-Net: Deep Groupwise Registration of Multispectral Images -- Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery -- Generalizations of Ripleys K-Function with Application to Space Curves -- Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates -- InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-Contrast Microstructural MRI -- Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases -- Learning-Based Optimization of the Under-Sampling Pattern in MRI -- Melanoma Recognition via Visual Attention -- Nonlinear Markov Random Fields Learned via Backpropagation -- Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler -- SHAMANN: Shared Memory Augmented Neural Networks -- Signet Ring Cell Detection With a Semi-supervised Learning Framework -- Spherical U-Net on Cortical Surfaces: Methods and Applications -- Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis. |
| Record Nr. | UNISA-996466326903316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Information Processing in Medical Imaging : 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings / / edited by Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao
| Information Processing in Medical Imaging : 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings / / edited by Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIX, 884 p. 517 illus., 331 illus. in color.) |
| Disciplina |
006.6
006.37 616.07540285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Computer science—Mathematics Health informatics Computers Operating systems (Computers) Image Processing and Computer Vision Artificial Intelligence Mathematics of Computing Health Informatics Models and Principles Operating Systems |
| ISBN | 3-030-20351-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Segmentation -- A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration -- Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology -- Semi-Supervised and Task-Driven Data Augmentation -- Classification and Inference -- Analyzing Brain Morphology on the Bag-of-Features Manifold -- Modeling and Inference of Spatio-Temporal Protein Dynamics Across Brain Networks -- Deep Learning -- InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction -- Adaptive Graph Convolution Pooling for Brain Surface Analysis -- On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging -- A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging -- Improved Disease Classification in Chest X-rays with Transferred Features from Report Generation -- Reconstruction -- Limited Angle Tomography Reconstruction: Synthetic Reconstruction via Unsupervised Sinogram Adaptation -- Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences -- Disease Modeling -- Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia -- Shape -- Minimizing Non-Holonomicity: Finding Sheets in Fibrous Structures -- Learning Low-Dimensional Representations of Shape Data Sets with Diffeomorphic Autoencoders -- Diffeomorphic Medial Modeling -- Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing -- Registration -- Local Optimal Transport for Functional Brain Template Estimation -- Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations -- Learning Motion -- Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting -- Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces -- Functional Imaging -- Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG -- A Novel Sparse Overlapping Modularized Gaussian Graphical Model for Functional Connectivity Estimation -- White Matter Imaging -- Asymmetry Spectrum Imaging for Baby Diffusion Tractography -- A Fast Fiber k-Nearest-Neighbor Algorithm with Application to Group-Wise White Matter Topography Analysis -- Posters -- 3D Organ Shape Reconstruction from Topogram Images -- A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation -- A Graph Model of the Lungs with MorphologyBased Structure for Tuberculosis Type Classification -- A Longitudinal Model for Tau Aggregation in Alzheimers Disease Based on Structural Connectivity -- Accurate Nuclear Segmentation with Center Vector Encoding -- Bayesian Longitudinal Modeling of Early Stage Parkinsons Disease Using DaTscan Images -- Brain Tumor Segmentation on MRI with Missing Modalities -- Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation -- DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction -- ECKO: Ensemble of Clustered Knockoffs for Robust Multivariate Inference on fMRI Data -- FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms -- Graph Convolutional Nets for Tool Presence Detection in Surgical Videos -- High-Order Oriented Cylindrical Flux for Curvilinear Structure Detection and Vessel Segmentation -- Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network -- Learning a Conditional Generative Model for Anatomical Shape Analysis -- Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness -- Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data -- Riemannian Geometry Learning for Disease Progression Modelling -- Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model -- Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices with Applications to Neuroimaging -- Simultaneous Spatial-temporal Decomposition of Connectome-Scale Brain Networks by Deep Sparse Recurrent Auto-Encoders -- Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention -- A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces -- A Geometric Framework for Feature Mappings in Multimodal Fusion of Brain Image Data -- A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data -- A Model for Elastic Evolution on Foliated Shapes -- Analyzing Mild Cognitive Impairment Progression via Multi-view Structural Learning -- New Graph-Blind Convolutional Network for Brain Connectome Data Analysis -- CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation -- Data-Driven Model Order Reduction For Diffeomorphic Image Registration -- DGR-Net: Deep Groupwise Registration of Multispectral Images -- Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery -- Generalizations of Ripleys K-Function with Application to Space Curves -- Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates -- InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-Contrast Microstructural MRI -- Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases -- Learning-Based Optimization of the Under-Sampling Pattern in MRI -- Melanoma Recognition via Visual Attention -- Nonlinear Markov Random Fields Learned via Backpropagation -- Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler -- SHAMANN: Shared Memory Augmented Neural Networks -- Signet Ring Cell Detection With a Semi-supervised Learning Framework -- Spherical U-Net on Cortical Surfaces: Methods and Applications -- Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis. |
| Record Nr. | UNINA-9910337849803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||