Autor Chung, Albert C.S
|
|
Documentos disponibles escritos por este autor (2)
Hacer una sugerencia Refinar búsqueda26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings / Chung, Albert C.S ; Gee, James C. ; Yushkevich, Paul A. ; Bao, Siqi
Título : 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings Tipo de documento: documento electrónico Autores: Chung, Albert C.S, ; Gee, James C., ; Yushkevich, Paul A., ; Bao, Siqi, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XIX, 884 p. 517 ilustraciones, 331 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-20351-1 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Inteligencia artificial Visión por computador Operating systems Modelos de Computación Informática de la Salud Matemáticas de la Computación Sistemas operativos (computadoras) Informática Médica Matemáticas Informática Índice Dewey: 006.37 Visión artificial Resumen: Este libro constituye las actas de la 26.ª Conferencia Internacional sobre Procesamiento de Información en Imágenes Médicas, IPMI 2019, celebrada en la Universidad de Ciencia y Tecnología de Hong Kong, Hong Kong, China, en junio de 2019. Los 69 artículos completos presentados en este volumen fueron cuidadosamente revisado y seleccionado entre 229 presentaciones. Estaban organizados en secciones temáticas sobre aprendizaje profundo y segmentación; clasificación e inferencia; reconstrucción; modelado de enfermedades; forma, registro; movimiento de aprendizaje; imágenes funcionales; e imágenes de materia blanca. El libro también incluye una serie de artículos posteriores. . Nota de contenido: 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 Growthto 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-supervisedLearning Framework -- Spherical U-Net on Cortical Surfaces: Methods and Applications -- Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis. 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 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings [documento electrónico] / Chung, Albert C.S, ; Gee, James C., ; Yushkevich, Paul A., ; Bao, Siqi, . - 1 ed. . - [s.l.] : Springer, 2019 . - XIX, 884 p. 517 ilustraciones, 331 ilustraciones en color.
ISBN : 978-3-030-20351-1
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Inteligencia artificial Visión por computador Operating systems Modelos de Computación Informática de la Salud Matemáticas de la Computación Sistemas operativos (computadoras) Informática Médica Matemáticas Informática Índice Dewey: 006.37 Visión artificial Resumen: Este libro constituye las actas de la 26.ª Conferencia Internacional sobre Procesamiento de Información en Imágenes Médicas, IPMI 2019, celebrada en la Universidad de Ciencia y Tecnología de Hong Kong, Hong Kong, China, en junio de 2019. Los 69 artículos completos presentados en este volumen fueron cuidadosamente revisado y seleccionado entre 229 presentaciones. Estaban organizados en secciones temáticas sobre aprendizaje profundo y segmentación; clasificación e inferencia; reconstrucción; modelado de enfermedades; forma, registro; movimiento de aprendizaje; imágenes funcionales; e imágenes de materia blanca. El libro también incluye una serie de artículos posteriores. . Nota de contenido: 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 Growthto 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-supervisedLearning Framework -- Spherical U-Net on Cortical Surfaces: Methods and Applications -- Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis. 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
Título : Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging : MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers Tipo de documento: documento electrónico Autores: Müller, Henning, ; Kelm, B. Michael, ; Arbel, Tal, ; Cai, Weidong, ; Cardoso, M. Jorge, ; Langs, Georg, ; Menze, Bjoern, ; Metaxas, Dimitris, ; Montillo, Albert, ; Wells III, William M., ; Zhang, Shaoting, ; Chung, Albert C.S, ; Jenkinson, Mark, ; Ribbens, Annemie, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XIII, 222 p. 75 ilustraciones ISBN/ISSN/DL: 978-3-319-61188-4 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 Informática Médica Inteligencia artificial Informática Estadistica matematica Sistemas de reconocimiento de patrones Informática de la Salud Probabilidad y Estadística en Informática Aplicaciones matemáticas en informática Reconocimiento de patrones automatizado Índice Dewey: 006.37 Visión artificial Resumen: Este libro constituye las actas posteriores al taller, exhaustivamente arbitradas, del Taller internacional sobre visión médica por computadora, MCV 2016, y del Taller internacional sobre modelos gráficos y bayesianos para imágenes biomédicas, BAMBI 2016, celebrado en Atenas, Grecia, en octubre de 2016. en conjunto con la 19.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2016. Los 13 artículos presentados en el taller MCV y los 6 artículos presentados en el taller BAMBI fueron cuidadosamente revisados y seleccionados entre numerosas presentaciones. El objetivo del taller MCV es explorar el uso de algoritmos de "grandes datos" para recopilar, organizar y aprender a partir de conjuntos de datos de imágenes médicas a gran escala y para la comprensión automática de imágenes médicas con fines generales. El taller BAMBI tiene como objetivo resaltar el potencial del uso de modelos gráficos de campo aleatorios o bayesianos para avanzar en la investigación en el análisis de imágenes biomédicas. Nota de contenido: Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases -- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases -- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images -- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images -- Inferring Disease Status by non-Parametric Probabilistic Embedding -- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images -- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study -- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker -- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation -- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images -- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features -- Representation Learning for Cross-Modality Classification -- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound -- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images -- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data -- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields -- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data -- Non-local Graph-based Regularization for Deformable Image Registration -- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. . 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 Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging : MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers [documento electrónico] / Müller, Henning, ; Kelm, B. Michael, ; Arbel, Tal, ; Cai, Weidong, ; Cardoso, M. Jorge, ; Langs, Georg, ; Menze, Bjoern, ; Metaxas, Dimitris, ; Montillo, Albert, ; Wells III, William M., ; Zhang, Shaoting, ; Chung, Albert C.S, ; Jenkinson, Mark, ; Ribbens, Annemie, . - 1 ed. . - [s.l.] : Springer, 2017 . - XIII, 222 p. 75 ilustraciones.
ISBN : 978-3-319-61188-4
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 Informática Médica Inteligencia artificial Informática Estadistica matematica Sistemas de reconocimiento de patrones Informática de la Salud Probabilidad y Estadística en Informática Aplicaciones matemáticas en informática Reconocimiento de patrones automatizado Índice Dewey: 006.37 Visión artificial Resumen: Este libro constituye las actas posteriores al taller, exhaustivamente arbitradas, del Taller internacional sobre visión médica por computadora, MCV 2016, y del Taller internacional sobre modelos gráficos y bayesianos para imágenes biomédicas, BAMBI 2016, celebrado en Atenas, Grecia, en octubre de 2016. en conjunto con la 19.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2016. Los 13 artículos presentados en el taller MCV y los 6 artículos presentados en el taller BAMBI fueron cuidadosamente revisados y seleccionados entre numerosas presentaciones. El objetivo del taller MCV es explorar el uso de algoritmos de "grandes datos" para recopilar, organizar y aprender a partir de conjuntos de datos de imágenes médicas a gran escala y para la comprensión automática de imágenes médicas con fines generales. El taller BAMBI tiene como objetivo resaltar el potencial del uso de modelos gráficos de campo aleatorios o bayesianos para avanzar en la investigación en el análisis de imágenes biomédicas. Nota de contenido: Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases -- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases -- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images -- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images -- Inferring Disease Status by non-Parametric Probabilistic Embedding -- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images -- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study -- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker -- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation -- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images -- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features -- Representation Learning for Cross-Modality Classification -- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound -- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images -- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data -- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields -- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data -- Non-local Graph-based Regularization for Deformable Image Registration -- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. . 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
