Título :	22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III
Tipo de documento:	documento electrónico
Autores:	Shen, Dinggang, ; Liu, Tianming, ; Peters, Terry M., ; Staib, Lawrence H., ; Essert, Caroline, ; Zhou, Sean, ; Yap, Pew-Thian, ; Khan, Ali,
Mención de edición:	1 ed.
Editorial:	[s.l.] : Springer
Fecha de publicación:	2019
Número de páginas:	XXXVIII, 888 p. 359 ilustraciones, 314 ilustraciones en color.
ISBN/ISSN/DL:	978-3-030-32248-9
Nota general:	Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave:	Visión por computador  Sistemas de reconocimiento de patrones  Inteligencia artificial  Informática Médica  Reconocimiento de patrones automatizado  Informática de la Salud
Índice Dewey:	006.37 Visión artificial
Resumen:	El conjunto de seis volúmenes LNCS 11764, 11765, 11766, 11767, 11768 y 11769 constituye las actas arbitradas de la 22.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2019, celebrada en Shenzhen, China, en octubre de 2019. Los 539 artículos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1730 presentaciones en un proceso de revisión doble ciego. Los artículos están organizados en las siguientes secciones temáticas: Parte I: imágenes ópticas; endoscopia; microscopía. Parte II: segmentación de imágenes; registro de imagen; imágenes cardiovasculares; crecimiento, desarrollo, atrofia y progresión. Parte III: reconstrucción y síntesis de neuroimágenes; segmentación de neuroimagen; imágenes por resonancia magnética ponderada por difusión; neuroimagen funcional (fMRI); neuroimagen variada. Parte IV: forma; predicción; detección y localización; aprendizaje automático; diagnóstico asistido por ordenador; Reconstrucción y síntesis de imágenes. Parte V: intervenciones asistidas por ordenador; MIC se encuentra con CAI. Parte VI: tomografía computarizada; Imágenes de rayos X.
Nota de contenido:	Neuroimage Reconstruction and Synthesis -- Isotropic MRI Super-Resolution Reconstruction with Multi-Scale Gradient Field Prior -- A Two-Stage Multi-Loss Super-Resolution Network For Arterial Spin Labeling Magnetic Resonance Imaging -- Model Learning: Primal Dual Networks for Fast MR imaging -- Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging -- Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework -- Deep Learning Based Framework for Direct Reconstruction of PET Images -- Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction -- Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans using Sparse Fidelity Loss and Adversarial Regularization -- Single Image Based Reconstruction of High Field-like MR Images -- Deep Neural Network for QSM Background Field Removal -- RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting -- RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting -- GANReDL: Medical Image enhancement using a generative adversarial network with real-order derivative induced loss functions -- Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks -- Semi-Supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control -- Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-Modal Neuroimages -- Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map -- CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading -- Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression -- Neuroimage Segmentation -- Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation -- 3D DilatedMulti-Fiber Network for Real-time Brain Tumor Segmentation in MRI -- Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants -- VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation -- Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning -- Scalable Neural Architecture Search for 3D Medical Image Segmentation -- Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images -- High Resolution Medical Image Segmentation using Data-swapping Method -- X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies -- Multi-View Semi-supervised 3D Whole Brain Segmentation with a Self-Ensemble Network -- CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke -- Brain Segmentation from k-space with End-to-end Recurrent Attention Network -- Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images -- CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion -- A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation -- U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets -- Generative adversarial network for segmentation of motion affected neonatal brain MRI -- Interactive deep editing framework for medical image segmentation -- Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices -- Improving Multi-Atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation -- Unsupervised deep learning for Bayesian brain MRI segmentation -- Online atlasing using an iterative centroid -- ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation -- Complete Fetal Head Compounding from Multi-View 3D Ultrasound -- SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation -- Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation -- RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation -- Deep Cascaded Attention Networks for Multi-task Brain Tumor Segmentation -- Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation -- 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation -- Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion -- Multi-task Attention-based Semi-supervised Learning for Medical Image Segmentation -- AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation -- Automated Parcellation of the Cortex using Structural Connectome Harmonics -- Hierarchical parcellation of the cerebellum -- Intrinsic Patch-based Cortical Anatomical Parcellation using Graph Convolutional Neural Network on Surface Manifold -- Cortical Surface Parcellation using Spherical Convolutional Neural Networks -- A Soft STAPLE Algorithm Combined with Anatomical Knowledge -- Diffusion Weighted Magnetic Resonance Imaging -- Multi-Stage Image Quality Assessment of Diffusion MRI via Semi-Supervised Nonlocal Residual Networks -- Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks -- Surface-based Tracking of U-fibers in the Superficial White Matter -- Probing Brain Micro-Architecture by Orientation Distribution Invariant Identification of Diffusion Compartments -- Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments -- Topographic Filtering of Tractograms as Vector Field Flows -- Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE -- Super-Resolved q-Space Deep Learning -- Joint Identification of Network Hub Nodes by Multivariate Graph Inference -- Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions -- Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling -- Optimal experimental design for biophysical modelling in multidimensional diffusion MRI -- DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography‏ -- Fast and Scalable Optimal Transport for Brain Tractograms -- A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes -- Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning -- Functional Neuroimaging (fMRI) -- Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life -- A matched filter decomposition of fMRI into resting and task components -- Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI -- Integrating Functional and Structural Connectivitiesvia Diffusion-Convolution-Bilinear Neural Network -- Invertible Network for Classification and Biomarker Selection for ASD -- Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data -- Revealing Functional Connectivity by Learning Graph Laplacian -- Constructing Multi-Scale Connectome Atlas by Learning Common Topology of Brain Networks -- Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale -- Identify Hierarchical Structures from Task-based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net -- A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI -- A Novel Graph Wavelet Model for Brain Multi-Scale Functional-structural Feature Fusion -- Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis -- Decoding brain functional connectivity implicated in AD and MCI -- Interpretable Feature Learning Using Multi-Output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis -- Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder -- Miscellaneous Neuroimaging -- Doubly Weak Supervision of Deep Learning Models for Head CT -- Detecting Acute Strokes from Non-Contrast CT Scan Data Using Deep Convolutional Neural Networks -- FocusNet: I mbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images -- Regression-based Line Detection Network for Delineation of Largely Deformed Brain Midline -- Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage -- Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network -- Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage -- Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i

22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III [documento electrónico] / Shen, Dinggang, ; Liu, Tianming, ; Peters, Terry M., ; Staib, Lawrence H., ; Essert, Caroline, ; Zhou, Sean, ; Yap, Pew-Thian, ; Khan, Ali,  . -  1 ed.  . - [s.l.] : Springer, 2019 . - XXXVIII, 888 p. 359 ilustraciones, 314 ilustraciones en color.
ISBN : 978-3-030-32248-9
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.

Palabras clave:	Visión por computador  Sistemas de reconocimiento de patrones  Inteligencia artificial  Informática Médica  Reconocimiento de patrones automatizado  Informática de la Salud
Índice Dewey:	006.37 Visión artificial
Resumen:	El conjunto de seis volúmenes LNCS 11764, 11765, 11766, 11767, 11768 y 11769 constituye las actas arbitradas de la 22.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2019, celebrada en Shenzhen, China, en octubre de 2019. Los 539 artículos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1730 presentaciones en un proceso de revisión doble ciego. Los artículos están organizados en las siguientes secciones temáticas: Parte I: imágenes ópticas; endoscopia; microscopía. Parte II: segmentación de imágenes; registro de imagen; imágenes cardiovasculares; crecimiento, desarrollo, atrofia y progresión. Parte III: reconstrucción y síntesis de neuroimágenes; segmentación de neuroimagen; imágenes por resonancia magnética ponderada por difusión; neuroimagen funcional (fMRI); neuroimagen variada. Parte IV: forma; predicción; detección y localización; aprendizaje automático; diagnóstico asistido por ordenador; Reconstrucción y síntesis de imágenes. Parte V: intervenciones asistidas por ordenador; MIC se encuentra con CAI. Parte VI: tomografía computarizada; Imágenes de rayos X.
Nota de contenido:	Neuroimage Reconstruction and Synthesis -- Isotropic MRI Super-Resolution Reconstruction with Multi-Scale Gradient Field Prior -- A Two-Stage Multi-Loss Super-Resolution Network For Arterial Spin Labeling Magnetic Resonance Imaging -- Model Learning: Primal Dual Networks for Fast MR imaging -- Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging -- Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework -- Deep Learning Based Framework for Direct Reconstruction of PET Images -- Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction -- Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans using Sparse Fidelity Loss and Adversarial Regularization -- Single Image Based Reconstruction of High Field-like MR Images -- Deep Neural Network for QSM Background Field Removal -- RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting -- RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting -- GANReDL: Medical Image enhancement using a generative adversarial network with real-order derivative induced loss functions -- Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks -- Semi-Supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control -- Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-Modal Neuroimages -- Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map -- CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading -- Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression -- Neuroimage Segmentation -- Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation -- 3D DilatedMulti-Fiber Network for Real-time Brain Tumor Segmentation in MRI -- Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants -- VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation -- Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning -- Scalable Neural Architecture Search for 3D Medical Image Segmentation -- Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images -- High Resolution Medical Image Segmentation using Data-swapping Method -- X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies -- Multi-View Semi-supervised 3D Whole Brain Segmentation with a Self-Ensemble Network -- CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke -- Brain Segmentation from k-space with End-to-end Recurrent Attention Network -- Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images -- CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion -- A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation -- U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets -- Generative adversarial network for segmentation of motion affected neonatal brain MRI -- Interactive deep editing framework for medical image segmentation -- Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices -- Improving Multi-Atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation -- Unsupervised deep learning for Bayesian brain MRI segmentation -- Online atlasing using an iterative centroid -- ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation -- Complete Fetal Head Compounding from Multi-View 3D Ultrasound -- SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation -- Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation -- RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation -- Deep Cascaded Attention Networks for Multi-task Brain Tumor Segmentation -- Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation -- 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation -- Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion -- Multi-task Attention-based Semi-supervised Learning for Medical Image Segmentation -- AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation -- Automated Parcellation of the Cortex using Structural Connectome Harmonics -- Hierarchical parcellation of the cerebellum -- Intrinsic Patch-based Cortical Anatomical Parcellation using Graph Convolutional Neural Network on Surface Manifold -- Cortical Surface Parcellation using Spherical Convolutional Neural Networks -- A Soft STAPLE Algorithm Combined with Anatomical Knowledge -- Diffusion Weighted Magnetic Resonance Imaging -- Multi-Stage Image Quality Assessment of Diffusion MRI via Semi-Supervised Nonlocal Residual Networks -- Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks -- Surface-based Tracking of U-fibers in the Superficial White Matter -- Probing Brain Micro-Architecture by Orientation Distribution Invariant Identification of Diffusion Compartments -- Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments -- Topographic Filtering of Tractograms as Vector Field Flows -- Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE -- Super-Resolved q-Space Deep Learning -- Joint Identification of Network Hub Nodes by Multivariate Graph Inference -- Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions -- Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling -- Optimal experimental design for biophysical modelling in multidimensional diffusion MRI -- DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography‏ -- Fast and Scalable Optimal Transport for Brain Tractograms -- A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes -- Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning -- Functional Neuroimaging (fMRI) -- Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life -- A matched filter decomposition of fMRI into resting and task components -- Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI -- Integrating Functional and Structural Connectivitiesvia Diffusion-Convolution-Bilinear Neural Network -- Invertible Network for Classification and Biomarker Selection for ASD -- Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data -- Revealing Functional Connectivity by Learning Graph Laplacian -- Constructing Multi-Scale Connectome Atlas by Learning Common Topology of Brain Networks -- Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale -- Identify Hierarchical Structures from Task-based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net -- A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI -- A Novel Graph Wavelet Model for Brain Multi-Scale Functional-structural Feature Fusion -- Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis -- Decoding brain functional connectivity implicated in AD and MCI -- Interpretable Feature Learning Using Multi-Output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis -- Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder -- Miscellaneous Neuroimaging -- Doubly Weak Supervision of Deep Learning Models for Head CT -- Detecting Acute Strokes from Non-Contrast CT Scan Data Using Deep Convolutional Neural Networks -- FocusNet: I mbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images -- Regression-based Line Detection Network for Delineation of Largely Deformed Brain Midline -- Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage -- Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network -- Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage -- Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i