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Adolescent Brain Cognitive Development Neurocognitive Prediction [[electronic resource] ] : First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru
| Adolescent Brain Cognitive Development Neurocognitive Prediction [[electronic resource] ] : First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 188 p. 57 illus., 49 illus. in color.) |
| Disciplina | 616.8047548 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Machine learning Mathematical statistics Data mining Image Processing and Computer Vision Machine Learning Probability and Statistics in Computer Science Data Mining and Knowledge Discovery |
| ISBN | 3-030-31901-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction -- Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet -- Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction -- Surface-based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019 -- Prediction of Fluid Intelligence From T1-Weighted Magnetic Resonance Images -- Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI -- Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry -- Predict Fluid Intelligence of Adolescent Using Ensemble Learning -- Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach -- Predicting Fluid intelligence from structural MRI using Random Forest regression -- Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data -- An AutoML Approach for the Prediction of Fluid Intelligence From MRI-Derived Features -- Predicting Fluid Intelligence from MRI images with Encoder-decoder Regularization -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology -- Ensemble Modeling of Neurocognitive Performance Using MRI-derived Brain Structure Volumes -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression -- Predicting fluid intelligence using anatomical measures within functionally defined brain networks -- Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs -- Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction -- Adolescent fluid intelligence prediction from regional brain volumes and cortical curvatures using BlockPC-XGBoost -- Cortical and Subcortical Contributions to Predicting Intelligence using 3D ConvNets. |
| Record Nr. | UNISA-996466429903316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Adolescent Brain Cognitive Development Neurocognitive Prediction : First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru
| Adolescent Brain Cognitive Development Neurocognitive Prediction : First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 188 p. 57 illus., 49 illus. in color.) |
| Disciplina | 616.8047548 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Machine learning Computer science - Mathematics Mathematical statistics Data mining Computer Vision Machine Learning Probability and Statistics in Computer Science Data Mining and Knowledge Discovery |
| ISBN | 3-030-31901-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction -- Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet -- Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction -- Surface-based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019 -- Prediction of Fluid Intelligence From T1-Weighted Magnetic Resonance Images -- Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI -- Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry -- Predict Fluid Intelligence of Adolescent Using Ensemble Learning -- Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach -- Predicting Fluid intelligence from structural MRI using Random Forest regression -- Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data -- An AutoML Approach for the Prediction of Fluid Intelligence From MRI-Derived Features -- Predicting Fluid Intelligence from MRI images with Encoder-decoder Regularization -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology -- Ensemble Modeling of Neurocognitive Performance Using MRI-derived Brain Structure Volumes -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression -- Predicting fluid intelligence using anatomical measures within functionally defined brain networks -- Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs -- Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction -- Adolescent fluid intelligence prediction from regional brain volumes and cortical curvatures using BlockPC-XGBoost -- Cortical and Subcortical Contributions to Predicting Intelligence using 3D ConvNets. |
| Record Nr. | UNINA-9910349275503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Information Processing in Medical Imaging [[electronic resource] ] : 23rd International Conference, IPMI 2013, Asilomar, CA, USA, June 28--July 3, 2013, Proceedings / / edited by James C. Gee, Sarang Joshi, Kilian M. Pohl, William M. Wells, Lilla Zöllei
| Information Processing in Medical Imaging [[electronic resource] ] : 23rd International Conference, IPMI 2013, Asilomar, CA, USA, June 28--July 3, 2013, Proceedings / / edited by James C. Gee, Sarang Joshi, Kilian M. Pohl, William M. Wells, Lilla Zöllei |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (XXIV, 782 p. 312 illus.) |
| Disciplina |
006.6
006.37 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Pattern recognition Health informatics Radiology Artificial intelligence Mathematical statistics Image Processing and Computer Vision Pattern Recognition Health Informatics Imaging / Radiology Artificial Intelligence Probability and Statistics in Computer Science |
| ISBN | 3-642-38868-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification -- Exploring High-Order Functional Interactions via Structurally-Weighted LASSO Models -- Feature-Based Alignment of Volumetric Multi-modal Images -- Bayesian Estimation of Regularization and Atlas Building in Diffeomorphic Image Registration -- Gradient Competition Anisotropy for Centerline Extraction and Segmentation of Spinal Cords -- Automated Segmentation of the Cerebellar Lobules Using Boundary Specific Classification and Evolution -- Tree-Space Statistics and Approximations for Large-Scale Analysis of Anatomical Trees -- Predicting Cognitive Data from Medical Images Using Sparse Linear Regression -- A Multiple Hypothesis Based Method for Particle Tracking and Its Extension for Cell Segmentation -- A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequences -- Multi-layer Deformation Estimation for Fluoroscopic Imaging -- Fiber Connectivity Integrated Brain Activation Detection -- Diffeomorphic Metric Mapping of Hybrid Diffusion Imaging Based on BFOR Signal Basis -- Hyperbolic Harmonic Brain Surface Registration with Curvature-Based Landmark Matching -- Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry -- Segmenting the Papillary Muscles and the Trabeculae from High Resolution Cardiac CT through Restoration of Topological Handles -- Data-Driven Interactive 3D Medical Image Segmentation Based on Structured Patch Model -- Sparse Deformable Models with Application to Cardiac Motion Analysis -- A Longitudinal Functional Analysis Framework for Analysis of White Matter Tract Statistics -- Groupwise Simultaneous Manifold Alignment for High-Resolution Dynamic MR Imaging of Respiratory Motion -- Conformal Mapping via Metric Optimization with Application for Cortical Label Fusion -- A Novel Sparse Group Gaussian Graphical Model for Functional Connectivity Estimation -- Joint Co-Segmentation and Registration of 3D Ultrasound Images -- Deformable Modeling Using a 3D Boundary Representation with Quadratic Constraints on the Branching Structure of the Blum Skeleton -- Sparse Projections of Medical Images onto Manifolds -- Efficient 3D Multi-region Prostate MRI Segmentation Using Dual Optimization -- Locality Preserving Non-negative Basis Learning with Graph Embedding -- Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images -- Joint Fractional Segmentation and Multi-tensor Estimation in Diffusion MRI -- Retrospective Estimation of the Susceptibility Driven Field Map for Distortion Correction in Echo Planar Imaging -- Group-Wise Cortical Correspondence via Sulcal Curve-Constrained Entropy Minimization -- Diffeomorphic Spectral Matching of Cortical Surfaces -- The Non-Local Bootstrap – Estimation of Uncertainty in Diffusion MRI -- Beyond Crossing Fibers: Tractography Exploiting Sub-voxel Fibre Dispersion and Neighbourhood Structure -- Learning from M/EEG Data with Variable Brain Activation Delays -- Unsupervised Learning of Functional Network Dynamics in Resting State fMRI -- Cohort-Level Brain Mapping: Learning Cognitive Atoms to Single Out Specialized Regions -- Torso Image Analysis Rapid Multi-organ Segmentation Using Context Integration and Discriminative Models -- Edge- and Detail-Preserving Sparse Image Representations for Deformable Registration of Chest MRI and CT Volumes -- Multimodal Surface Matching: Fast and Generalisable Cortical Registration Using Discrete Optimisation -- Globally Optimal Cortical Surface Matching with Exact Landmark Correspondence -- Joint Learning of Appearance and Transformation for Predicting Brain MR Image Registration -- Automatic Prostate MR Image Segmentation with Sparse Label Propagation and Domain-Specific Manifold Regularization -- Moving Frames for Heart Fiber Geometry -- Structural Brain Network Constrained Neuroimaging Marker Identification for Predicting Cognitive Functions -- Multi-atlas Segmentation with Robust Label Transfer and Label Fusion -- A Hierarchical Geodesic Model for Diffeomorphic Longitudinal Shape Analysis -- Active Testing Search for Point Cloud Matching -- Relating Fisher Information to Detectability of Changes in Nodule Characteristics with CT -- Adaptive Multi-modal Particle Filtering for Probabilistic White Matter Tractography -- Can T2 -Spectroscopy Resolve Submicrometer Axon Diameters? -- Dictionary Learning on the Manifold of Square Root Densities and Application to Reconstruction of Diffusion Propagator Fields -- Diseased Region Detection of Longitudinal Knee MRI Data -- Model Selection and Estimation of Multi-compartment Models in Diffusion MRI with a Rician Noise Model -- Bayesian Segmentation of Atrium Wall Using Globally-Optimal Graph Cuts on 3D Meshes -- Using Region Trajectories to Construct an Accurate and Efficient Polyaffine Transform Model -- Extracting Evolving Pathologies via Spectral Clustering -- Construction of Multi-scale Common Brain Networks Based on DICCCOL -- Rotation Invariant Features for HARDI -- Geodesic Shape Regression in the Framework of Currents -- Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering -- Reliable Selection of the Number of Fascicles in Diffusion Images by Estimation of the Generalization Error -- IDiff: Irrotational Diffeomorphisms for Computational Anatomy -- Joint Generative Modeling of Imaging and Genetics. |
| Record Nr. | UNISA-996465835903316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Information Processing in Medical Imaging [[electronic resource] ] : 23rd International Conference, IPMI 2013, Asilomar, CA, USA, June 28--July 3, 2013, Proceedings / / edited by James C. Gee, Sarang Joshi, Kilian M. Pohl, William M. Wells, Lilla Zöllei
| Information Processing in Medical Imaging [[electronic resource] ] : 23rd International Conference, IPMI 2013, Asilomar, CA, USA, June 28--July 3, 2013, Proceedings / / edited by James C. Gee, Sarang Joshi, Kilian M. Pohl, William M. Wells, Lilla Zöllei |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (XXIV, 782 p. 312 illus.) |
| Disciplina |
006.6
006.37 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Pattern recognition Health informatics Radiology Artificial intelligence Mathematical statistics Image Processing and Computer Vision Pattern Recognition Health Informatics Imaging / Radiology Artificial Intelligence Probability and Statistics in Computer Science |
| ISBN | 3-642-38868-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification -- Exploring High-Order Functional Interactions via Structurally-Weighted LASSO Models -- Feature-Based Alignment of Volumetric Multi-modal Images -- Bayesian Estimation of Regularization and Atlas Building in Diffeomorphic Image Registration -- Gradient Competition Anisotropy for Centerline Extraction and Segmentation of Spinal Cords -- Automated Segmentation of the Cerebellar Lobules Using Boundary Specific Classification and Evolution -- Tree-Space Statistics and Approximations for Large-Scale Analysis of Anatomical Trees -- Predicting Cognitive Data from Medical Images Using Sparse Linear Regression -- A Multiple Hypothesis Based Method for Particle Tracking and Its Extension for Cell Segmentation -- A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequences -- Multi-layer Deformation Estimation for Fluoroscopic Imaging -- Fiber Connectivity Integrated Brain Activation Detection -- Diffeomorphic Metric Mapping of Hybrid Diffusion Imaging Based on BFOR Signal Basis -- Hyperbolic Harmonic Brain Surface Registration with Curvature-Based Landmark Matching -- Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry -- Segmenting the Papillary Muscles and the Trabeculae from High Resolution Cardiac CT through Restoration of Topological Handles -- Data-Driven Interactive 3D Medical Image Segmentation Based on Structured Patch Model -- Sparse Deformable Models with Application to Cardiac Motion Analysis -- A Longitudinal Functional Analysis Framework for Analysis of White Matter Tract Statistics -- Groupwise Simultaneous Manifold Alignment for High-Resolution Dynamic MR Imaging of Respiratory Motion -- Conformal Mapping via Metric Optimization with Application for Cortical Label Fusion -- A Novel Sparse Group Gaussian Graphical Model for Functional Connectivity Estimation -- Joint Co-Segmentation and Registration of 3D Ultrasound Images -- Deformable Modeling Using a 3D Boundary Representation with Quadratic Constraints on the Branching Structure of the Blum Skeleton -- Sparse Projections of Medical Images onto Manifolds -- Efficient 3D Multi-region Prostate MRI Segmentation Using Dual Optimization -- Locality Preserving Non-negative Basis Learning with Graph Embedding -- Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images -- Joint Fractional Segmentation and Multi-tensor Estimation in Diffusion MRI -- Retrospective Estimation of the Susceptibility Driven Field Map for Distortion Correction in Echo Planar Imaging -- Group-Wise Cortical Correspondence via Sulcal Curve-Constrained Entropy Minimization -- Diffeomorphic Spectral Matching of Cortical Surfaces -- The Non-Local Bootstrap – Estimation of Uncertainty in Diffusion MRI -- Beyond Crossing Fibers: Tractography Exploiting Sub-voxel Fibre Dispersion and Neighbourhood Structure -- Learning from M/EEG Data with Variable Brain Activation Delays -- Unsupervised Learning of Functional Network Dynamics in Resting State fMRI -- Cohort-Level Brain Mapping: Learning Cognitive Atoms to Single Out Specialized Regions -- Torso Image Analysis Rapid Multi-organ Segmentation Using Context Integration and Discriminative Models -- Edge- and Detail-Preserving Sparse Image Representations for Deformable Registration of Chest MRI and CT Volumes -- Multimodal Surface Matching: Fast and Generalisable Cortical Registration Using Discrete Optimisation -- Globally Optimal Cortical Surface Matching with Exact Landmark Correspondence -- Joint Learning of Appearance and Transformation for Predicting Brain MR Image Registration -- Automatic Prostate MR Image Segmentation with Sparse Label Propagation and Domain-Specific Manifold Regularization -- Moving Frames for Heart Fiber Geometry -- Structural Brain Network Constrained Neuroimaging Marker Identification for Predicting Cognitive Functions -- Multi-atlas Segmentation with Robust Label Transfer and Label Fusion -- A Hierarchical Geodesic Model for Diffeomorphic Longitudinal Shape Analysis -- Active Testing Search for Point Cloud Matching -- Relating Fisher Information to Detectability of Changes in Nodule Characteristics with CT -- Adaptive Multi-modal Particle Filtering for Probabilistic White Matter Tractography -- Can T2 -Spectroscopy Resolve Submicrometer Axon Diameters? -- Dictionary Learning on the Manifold of Square Root Densities and Application to Reconstruction of Diffusion Propagator Fields -- Diseased Region Detection of Longitudinal Knee MRI Data -- Model Selection and Estimation of Multi-compartment Models in Diffusion MRI with a Rician Noise Model -- Bayesian Segmentation of Atrium Wall Using Globally-Optimal Graph Cuts on 3D Meshes -- Using Region Trajectories to Construct an Accurate and Efficient Polyaffine Transform Model -- Extracting Evolving Pathologies via Spectral Clustering -- Construction of Multi-scale Common Brain Networks Based on DICCCOL -- Rotation Invariant Features for HARDI -- Geodesic Shape Regression in the Framework of Currents -- Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering -- Reliable Selection of the Number of Fascicles in Diffusion Images by Estimation of the Generalization Error -- IDiff: Irrotational Diffeomorphisms for Computational Anatomy -- Joint Generative Modeling of Imaging and Genetics. |
| Record Nr. | UNINA-9910484818103321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||