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TÃtulo : Combinatorial Image Analysis : 19th International Workshop, IWCIA 2018, Porto, Portugal, November 22–24, 2018, Proceedings / Tipo de documento: documento electrónico Autores: Barneva, Reneta P., ; Brimkov, Valentin E., ; Tavares, João Manuel RS, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XII, 237 p. 117 ilustraciones, 61 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-05288-1 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Gráficos de computadora IngenierÃa de software Informática Médica Computadoras Propósitos especiales Informática de la Salud Operaciones de TI Sistemas de propósito especial y basados ​​en aplicaciones Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del 19º Taller Internacional sobre Análisis Combinatorio de Imágenes, IWCIA 2018, celebrado en Oporto, Portugal, en noviembre de 2018. Los 18 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 32 presentaciones. Los artÃculos se agrupan en dos secciones. El primero incluye nueve artÃculos dedicados a los fundamentos teóricos del análisis combinatorio de imágenes, incluida la geometrÃa y topologÃa digitales, gramáticas de matrices, mosaicos y patrones, geometrÃa discreta en cuadrÃculas no rectangulares y otras herramientas técnicas para el análisis de imágenes. La segunda parte incluye nueve artÃculos que presentan investigaciones basadas en aplicaciones sobre temas como tomografÃa discreta, segmentación de imágenes, análisis de texturas e imágenes médicas. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 19th International Workshop on Combinatorial Image Analysis, IWCIA 2018, held in Porto, Portugal, in November 2018. The 18 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers are grouped into two sections. The first one includes nine papers devoted to theoretical foundations of combinatorial image analysis, including digital geometry and topology, array grammars, tilings and patterns, discrete geometry in non-rectangular grids, and other technical tools for image analysis. The second part includes nine papers presenting application-driven research on topics such as discrete tomography, image segmentation, texture analysis, and medical imaging. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Combinatorial Image Analysis : 19th International Workshop, IWCIA 2018, Porto, Portugal, November 22–24, 2018, Proceedings / [documento electrónico] / Barneva, Reneta P., ; Brimkov, Valentin E., ; Tavares, João Manuel RS, . - 1 ed. . - [s.l.] : Springer, 2018 . - XII, 237 p. 117 ilustraciones, 61 ilustraciones en color.
ISBN : 978-3-030-05288-1
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Gráficos de computadora IngenierÃa de software Informática Médica Computadoras Propósitos especiales Informática de la Salud Operaciones de TI Sistemas de propósito especial y basados ​​en aplicaciones Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del 19º Taller Internacional sobre Análisis Combinatorio de Imágenes, IWCIA 2018, celebrado en Oporto, Portugal, en noviembre de 2018. Los 18 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 32 presentaciones. Los artÃculos se agrupan en dos secciones. El primero incluye nueve artÃculos dedicados a los fundamentos teóricos del análisis combinatorio de imágenes, incluida la geometrÃa y topologÃa digitales, gramáticas de matrices, mosaicos y patrones, geometrÃa discreta en cuadrÃculas no rectangulares y otras herramientas técnicas para el análisis de imágenes. La segunda parte incluye nueve artÃculos que presentan investigaciones basadas en aplicaciones sobre temas como tomografÃa discreta, segmentación de imágenes, análisis de texturas e imágenes médicas. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 19th International Workshop on Combinatorial Image Analysis, IWCIA 2018, held in Porto, Portugal, in November 2018. The 18 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers are grouped into two sections. The first one includes nine papers devoted to theoretical foundations of combinatorial image analysis, including digital geometry and topology, array grammars, tilings and patterns, discrete geometry in non-rectangular grids, and other technical tools for image analysis. The second part includes nine papers presenting application-driven research on topics such as discrete tomography, image segmentation, texture analysis, and medical imaging. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications / Barneva, Reneta P. ; Brimkov, Valentin E. ; Tavares, João Manuel RS
TÃtulo : Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications : 5th International Symposium, CompIMAGE 2016, Niagara Falls, NY, USA, September 21-23, 2016, Revised Selected Papers Tipo de documento: documento electrónico Autores: Barneva, Reneta P., ; Brimkov, Valentin E., ; Tavares, João Manuel RS, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XII, 259 p. 95 ilustraciones ISBN/ISSN/DL: 978-3-319-54609-4 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Gráficos de computadora Red de computadoras Protección de datos Procesamiento de datos CriptografÃa Cifrado de datos (Informática) Redes de comunicación informática Seguridad de datos e información MinerÃa de datos y descubrimiento de conocimientos CriptologÃa Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas posteriores a la conferencia de la Quinta Conferencia Internacional sobre Modelado Computacional de Objetos Presentados en Imágenes, CompIMAGE 2016, celebrada en Niagara Falls, Nueva York, EE. UU., en septiembre de 2016. Los 18 artÃculos completos revisados ​​se presentan junto con 1 artÃculo invitado fueron cuidadosamente revisados ​​y seleccionados entre 30 presentaciones. Los artÃculos cubren los siguientes temas: contribuciones teóricas y contribuciones impulsadas por aplicaciones. Nota de contenido: Model Development and Incremental Learning Based on Case-Based Reasoning for Signal and Image Analysis -- CVT-Based 3D Image Segmentation for Quality Tetrahedral Meshing -- Structuring Digital Spaces by Path-partition Induced Closure Operators on Graphs -- Atypical (Rare) Elements Detection - A Conditional Nonparametric Approach -- Finding Shortest Isothetic Path inside a 3D Digital Object -- Unified Characterization of P-Simple Points in Triangular, Square, and Hexagonal Grids -- Concepts of Binary Morphological Operations Dilation and Erosion on the Triangular Grid -- Boundary and Shape Complexity of a Digital Object -- Interior and Exterior Shape Representations Using the Screened Poisson Equation -- Picture Scanning Automata -- Two-Dimensional Input-Revolving Automata -- Direct Phasing of Crystalline Materials from X-ray Powder Diffraction -- Detection of Counterfeit Coins Based on Modeling and Restoration of 3D Images -- Automated Brain Tumor Diagnosis and Severity Analysis from Brain MRI.-Medical Image Segmentation Using Improved Affinity Propagation -- Simple Signed-Distance Function Depth Calculation Applied to Measurement of the fMRI BOLD Hemodynamic Response Function in Human Visual Cortex -- A Study of Children Facial Recognition for Privacy in Smart TV -- Scrambling Cryptography Using Programmable SLM-based Filter for Video Streaming over a WDM Network -- An Accelerated H.264/AVC Encoder on Graphic Processing Unit for UAV Videos. Tipo de medio : Computadora Summary : This book constitutes the refereed post-conference proceedings of the 5th International Conference on Computational Modeling of Objects Presented in Images, CompIMAGE 2016, held in Niagara Falls, NY, USA, in September 2016. The 18 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 30 submissions. The papers cover the following topics: theoretical contributions and application-driven contributions. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications : 5th International Symposium, CompIMAGE 2016, Niagara Falls, NY, USA, September 21-23, 2016, Revised Selected Papers [documento electrónico] / Barneva, Reneta P., ; Brimkov, Valentin E., ; Tavares, João Manuel RS, . - 1 ed. . - [s.l.] : Springer, 2017 . - XII, 259 p. 95 ilustraciones.
ISBN : 978-3-319-54609-4
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Gráficos de computadora Red de computadoras Protección de datos Procesamiento de datos CriptografÃa Cifrado de datos (Informática) Redes de comunicación informática Seguridad de datos e información MinerÃa de datos y descubrimiento de conocimientos CriptologÃa Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas posteriores a la conferencia de la Quinta Conferencia Internacional sobre Modelado Computacional de Objetos Presentados en Imágenes, CompIMAGE 2016, celebrada en Niagara Falls, Nueva York, EE. UU., en septiembre de 2016. Los 18 artÃculos completos revisados ​​se presentan junto con 1 artÃculo invitado fueron cuidadosamente revisados ​​y seleccionados entre 30 presentaciones. Los artÃculos cubren los siguientes temas: contribuciones teóricas y contribuciones impulsadas por aplicaciones. Nota de contenido: Model Development and Incremental Learning Based on Case-Based Reasoning for Signal and Image Analysis -- CVT-Based 3D Image Segmentation for Quality Tetrahedral Meshing -- Structuring Digital Spaces by Path-partition Induced Closure Operators on Graphs -- Atypical (Rare) Elements Detection - A Conditional Nonparametric Approach -- Finding Shortest Isothetic Path inside a 3D Digital Object -- Unified Characterization of P-Simple Points in Triangular, Square, and Hexagonal Grids -- Concepts of Binary Morphological Operations Dilation and Erosion on the Triangular Grid -- Boundary and Shape Complexity of a Digital Object -- Interior and Exterior Shape Representations Using the Screened Poisson Equation -- Picture Scanning Automata -- Two-Dimensional Input-Revolving Automata -- Direct Phasing of Crystalline Materials from X-ray Powder Diffraction -- Detection of Counterfeit Coins Based on Modeling and Restoration of 3D Images -- Automated Brain Tumor Diagnosis and Severity Analysis from Brain MRI.-Medical Image Segmentation Using Improved Affinity Propagation -- Simple Signed-Distance Function Depth Calculation Applied to Measurement of the fMRI BOLD Hemodynamic Response Function in Human Visual Cortex -- A Study of Children Facial Recognition for Privacy in Smart TV -- Scrambling Cryptography Using Programmable SLM-based Filter for Video Streaming over a WDM Network -- An Accelerated H.264/AVC Encoder on Graphic Processing Unit for UAV Videos. Tipo de medio : Computadora Summary : This book constitutes the refereed post-conference proceedings of the 5th International Conference on Computational Modeling of Objects Presented in Images, CompIMAGE 2016, held in Niagara Falls, NY, USA, in September 2016. The 18 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 30 submissions. The papers cover the following topics: theoretical contributions and application-driven contributions. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings Tipo de documento: documento electrónico Autores: Stoyanov, Danail, ; Taylor, Zeike, ; Carneiro, Gustavo, ; Syeda-Mahmood, Tanveer, ; Martel, Anne, ; Maier-Hein, Lena, ; Tavares, João Manuel RS, ; Bradley, Andrew, ; Papa, João Paulo, ; Belagiannis, Vasileios, ; Nascimento, Jacinto C., ; Lu, Zhi, ; Conjeti, Sailesh, ; Moradi, Mehdi, ; Greenspan, Hayit, ; Madabhushi, Anant, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XVII, 387 p. 197 ilustraciones, 149 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-00889-5 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Informática Médica Ciencias sociales Protección de datos Informática de la Salud Computadoras y Educación Aplicación informática en ciencias sociales y del comportamiento. Seguridad de datos e información Clasificación: 006.3 Resumen: Este libro constituye las actas conjuntas arbitradas del 4.º Taller internacional sobre aprendizaje profundo en análisis de imágenes médicas, DLMIA 2018, y el 8.º Taller internacional sobre aprendizaje multimodal para el apoyo a las decisiones clÃnicas, ML-CDS 2018, celebrado junto con la 21.ª Conferencia internacional sobre Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 39 artÃculos completos presentados en DLMIA 2018 y los 4 artÃculos completos presentados en ML-CDS 2018 fueron cuidadosamente revisados ​​y seleccionados entre 85 presentaciones a DLMIA y 6 presentaciones a ML-CDS. Los artÃculos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artÃculos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clÃnicas. Nota de contenido: Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior -- Weakly Supervised Localisation for Fetal Ultrasound Images -- Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images -- Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks -- Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease -- Contextual Additive Networks to Efficiently Boost 3D Image Segmentations -- Longitudinal detection of radiological abnormalities with time-modulated LSTM -- SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays -- Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy -- Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-Organizing Maps -- Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images -- Deep semi-supervised segmentation with weight-averaged consistency targets -- Focal Dice Loss and Image Dilation for Brain Tumor Segmentation -- Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography -- Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and Wasserstein autoencoders: application to epilepsy detection -- Automatic myocardial strain imaging in echocardiography using deep learning -- 3D Convolutional Neural Networks for Classification of Functional Connectomes -- Computed Tomography Image Enhancement using 3D Convolutional Neural Network -- Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning -- A Unified Framework Integrating Recurrent Fully-convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data -- Learning Optimal Deep Projection of 18 F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes -- Learning to Segment Medical Images with Scribble-Supervision Alone -- Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration -- TreeNet: Multi-Loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees -- Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation -- UOLO - automatic object detection and segmentation in biomedical images -- Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks -- Multi-Scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification -- Nonlinear adaptively learned optimization for object localization in 3D medical images -- Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network -- UNet++: A Nested U-Net Architecture for Medical Image Segmentation -- MTMR-Net: Multi-Task Deep Learning with Margin Ranking Lossfor Lung Nodule Analysis -- PIMMS: Permutation Invariant Multi-Modal Segmentation -- Handling Missing Annotations for Semantic Segmentation with Deep ConvNets -- 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation -- ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans -- Unpaired Deep Cross-modality Synthesis with Fast Training -- Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification -- Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN -- A Multi-Scale Multiple Sclerosis Lesion Change Detection in a Multi-Sequence MRI -- Multi-task Sparse Low-rank Learning for Multi-classification of Parkinson's Disease -- Optic Disc segmentation in Retinal Fundus Images using Fully Convolutional Network and Removal of False-positives Based on Shape Features -- Integrating deformable modeling with 3D deep neural network segmentation. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings [documento electrónico] / Stoyanov, Danail, ; Taylor, Zeike, ; Carneiro, Gustavo, ; Syeda-Mahmood, Tanveer, ; Martel, Anne, ; Maier-Hein, Lena, ; Tavares, João Manuel RS, ; Bradley, Andrew, ; Papa, João Paulo, ; Belagiannis, Vasileios, ; Nascimento, Jacinto C., ; Lu, Zhi, ; Conjeti, Sailesh, ; Moradi, Mehdi, ; Greenspan, Hayit, ; Madabhushi, Anant, . - 1 ed. . - [s.l.] : Springer, 2018 . - XVII, 387 p. 197 ilustraciones, 149 ilustraciones en color.
ISBN : 978-3-030-00889-5
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Informática Médica Ciencias sociales Protección de datos Informática de la Salud Computadoras y Educación Aplicación informática en ciencias sociales y del comportamiento. Seguridad de datos e información Clasificación: 006.3 Resumen: Este libro constituye las actas conjuntas arbitradas del 4.º Taller internacional sobre aprendizaje profundo en análisis de imágenes médicas, DLMIA 2018, y el 8.º Taller internacional sobre aprendizaje multimodal para el apoyo a las decisiones clÃnicas, ML-CDS 2018, celebrado junto con la 21.ª Conferencia internacional sobre Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 39 artÃculos completos presentados en DLMIA 2018 y los 4 artÃculos completos presentados en ML-CDS 2018 fueron cuidadosamente revisados ​​y seleccionados entre 85 presentaciones a DLMIA y 6 presentaciones a ML-CDS. Los artÃculos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artÃculos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clÃnicas. Nota de contenido: Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior -- Weakly Supervised Localisation for Fetal Ultrasound Images -- Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images -- Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks -- Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease -- Contextual Additive Networks to Efficiently Boost 3D Image Segmentations -- Longitudinal detection of radiological abnormalities with time-modulated LSTM -- SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays -- Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy -- Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-Organizing Maps -- Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images -- Deep semi-supervised segmentation with weight-averaged consistency targets -- Focal Dice Loss and Image Dilation for Brain Tumor Segmentation -- Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography -- Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and Wasserstein autoencoders: application to epilepsy detection -- Automatic myocardial strain imaging in echocardiography using deep learning -- 3D Convolutional Neural Networks for Classification of Functional Connectomes -- Computed Tomography Image Enhancement using 3D Convolutional Neural Network -- Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning -- A Unified Framework Integrating Recurrent Fully-convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data -- Learning Optimal Deep Projection of 18 F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes -- Learning to Segment Medical Images with Scribble-Supervision Alone -- Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration -- TreeNet: Multi-Loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees -- Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation -- UOLO - automatic object detection and segmentation in biomedical images -- Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks -- Multi-Scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification -- Nonlinear adaptively learned optimization for object localization in 3D medical images -- Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network -- UNet++: A Nested U-Net Architecture for Medical Image Segmentation -- MTMR-Net: Multi-Task Deep Learning with Margin Ranking Lossfor Lung Nodule Analysis -- PIMMS: Permutation Invariant Multi-Modal Segmentation -- Handling Missing Annotations for Semantic Segmentation with Deep ConvNets -- 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation -- ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans -- Unpaired Deep Cross-modality Synthesis with Fast Training -- Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification -- Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN -- A Multi-Scale Multiple Sclerosis Lesion Change Detection in a Multi-Sequence MRI -- Multi-task Sparse Low-rank Learning for Multi-classification of Parkinson's Disease -- Optic Disc segmentation in Retinal Fundus Images using Fully Convolutional Network and Removal of False-positives Based on Shape Features -- Integrating deformable modeling with 3D deep neural network segmentation. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings Tipo de documento: documento electrónico Autores: Cardoso, M. Jorge, ; Arbel, Tal, ; Carneiro, Gustavo, ; Syeda-Mahmood, Tanveer, ; Tavares, João Manuel RS, ; Moradi, Mehdi, ; Bradley, Andrew, ; Greenspan, Hayit, ; Papa, João Paulo, ; Madabhushi, Anant, ; Nascimento, Jacinto C., ; Cardoso, Jaime S., ; Belagiannis, Vasileios, ; Lu, Zhi, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XIX, 385 p. 169 ilustraciones ISBN/ISSN/DL: 978-3-319-67558-9 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Inteligencia artificial Informática Médica Bioinformática diseño lógico Informática de la Salud BiologÃa Computacional y de Sistemas Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Tercer Taller Internacional sobre Aprendizaje Profundo en Análisis de Imágenes Médicas, DLMIA 2017, y el 6º Taller Internacional sobre Aprendizaje Multimodal para el Apoyo a la Decisión ClÃnica, ML-CDS 2017, celebrado junto con la 20ª Conferencia Internacional sobre Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, en la ciudad de Québec, QC, Canadá, en septiembre de 2017. Los 38 artÃculos completos presentados en DLMIA 2017 y los 5 artÃculos completos presentados en ML-CDS 2017 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artÃculos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clÃnicas. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings [documento electrónico] / Cardoso, M. Jorge, ; Arbel, Tal, ; Carneiro, Gustavo, ; Syeda-Mahmood, Tanveer, ; Tavares, João Manuel RS, ; Moradi, Mehdi, ; Bradley, Andrew, ; Greenspan, Hayit, ; Papa, João Paulo, ; Madabhushi, Anant, ; Nascimento, Jacinto C., ; Cardoso, Jaime S., ; Belagiannis, Vasileios, ; Lu, Zhi, . - 1 ed. . - [s.l.] : Springer, 2017 . - XIX, 385 p. 169 ilustraciones.
ISBN : 978-3-319-67558-9
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Inteligencia artificial Informática Médica Bioinformática diseño lógico Informática de la Salud BiologÃa Computacional y de Sistemas Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Tercer Taller Internacional sobre Aprendizaje Profundo en Análisis de Imágenes Médicas, DLMIA 2017, y el 6º Taller Internacional sobre Aprendizaje Multimodal para el Apoyo a la Decisión ClÃnica, ML-CDS 2017, celebrado junto con la 20ª Conferencia Internacional sobre Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, en la ciudad de Québec, QC, Canadá, en septiembre de 2017. Los 38 artÃculos completos presentados en DLMIA 2017 y los 5 artÃculos completos presentados en ML-CDS 2017 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artÃculos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clÃnicas. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : FEM Analysis of the Human Knee Joint : A Review Tipo de documento: documento electrónico Autores: Trad, Zahra, ; Barkaoui, Abdelwahed, ; Chafra, Moez, ; Tavares, João Manuel RS, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XVII, 79 p. 39 ilustraciones en color. ISBN/ISSN/DL: 978-3-319-74158-1 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: IngenierÃa Biomédica Mecánica Aplicada Sólidos Biomateriales Ingenieria asistida por computadora IngenierÃa Biomédica y BioingenierÃa Mecánica de sólidos IngenierÃa y Diseño Asistidos por Computador (CAD CAE) Clasificación: 610.28 Resumen: En los últimos años numerosas investigaciones cientÃficas han estudiado el papel anatómico, biomecánico y funcional de las estructuras implicadas en la articulación de la rodilla humana. El Método de los Elementos Finitos (MEF) se ha considerado una herramienta interesante para estudiar y simular biosistemas. Se ha utilizado ampliamente para analizar la articulación de la rodilla y varios tipos de enfermedades de la rodilla y procedimientos de rehabilitación como la osteotomÃa tibial alta (HTO). Este trabajo presenta una revisión del análisis FEM de la articulación de la rodilla humana y la cirugÃa de rodilla HTO, y discute cuán adecuada es esta herramienta computacional para este tipo de aplicaciones biomédicas. Por lo tanto, se revisan varios estudios que abordan la articulación de la rodilla basados ​​en el Análisis de Elementos Finitos (FEA), y se proporciona una descripción general de los estudios clÃnicos y biomecánicos sobre la optimización del ángulo de corrección de la cirugÃa de rodilla posoperatoria. Nota de contenido: Introduction -- 1. Finite element models of the knee joint -- 1.1 Knee joint models geometries -- 1.2 Material properties of hard and soft tissues -- 1.2.1 Material properties of articular cartilage -- 1.2.2 Material properties of menisci -- 1.2.3 Material properties of ligaments -- 1.2.4 Material properties of bony structure -- 2. Finite Element Analysis applications in knee joint biomechanical studies -- 2.1 Current FEA applications on ligament injury -- 2.2 Current FEA applications on meniscus injury -- 2.3 Current FEA applications on knee joint contact analysis and cartilage disease -- 3. Overview of high tibial osteotomy and optimization of correction angle -- 3.1 High tibial osteotomy definition -- 3.2 Current FEA studies on HTO procedure -- 3.3 Current clinical studies on optimizing correction angle -- 3.4 Current biomechanical studies on optimizing correction angle -- 4. Conclusions and future work -- References. Tipo de medio : Computadora Summary : In recent years, numerous scientific investigations have studied the anatomical, biomechanical and functional role of structures involved in the human knee joint. The Finite Element Method (FEM) has been seen as an interesting tool to study and simulate biosystems. It has been extensively used to analyse the knee joint and various types of knee diseases and rehabilitation procedures such as the High Tibial Osteotomy (HTO). This work presents a review on FEM analysis of the human knee joint and HTO knee surgery, and discusses how adequate this computational tool is for this type of biomedical applications. Hence, various studies addressing the knee joint based on Finite Element Analysis (FEA) are reviewed, and an overview of clinical and biomechanical studies on the optimization of the correction angle of the postoperative knee surgery is provided. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] FEM Analysis of the Human Knee Joint : A Review [documento electrónico] / Trad, Zahra, ; Barkaoui, Abdelwahed, ; Chafra, Moez, ; Tavares, João Manuel RS, . - 1 ed. . - [s.l.] : Springer, 2018 . - XVII, 79 p. 39 ilustraciones en color.
ISBN : 978-3-319-74158-1
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: IngenierÃa Biomédica Mecánica Aplicada Sólidos Biomateriales Ingenieria asistida por computadora IngenierÃa Biomédica y BioingenierÃa Mecánica de sólidos IngenierÃa y Diseño Asistidos por Computador (CAD CAE) Clasificación: 610.28 Resumen: En los últimos años numerosas investigaciones cientÃficas han estudiado el papel anatómico, biomecánico y funcional de las estructuras implicadas en la articulación de la rodilla humana. El Método de los Elementos Finitos (MEF) se ha considerado una herramienta interesante para estudiar y simular biosistemas. Se ha utilizado ampliamente para analizar la articulación de la rodilla y varios tipos de enfermedades de la rodilla y procedimientos de rehabilitación como la osteotomÃa tibial alta (HTO). Este trabajo presenta una revisión del análisis FEM de la articulación de la rodilla humana y la cirugÃa de rodilla HTO, y discute cuán adecuada es esta herramienta computacional para este tipo de aplicaciones biomédicas. Por lo tanto, se revisan varios estudios que abordan la articulación de la rodilla basados ​​en el Análisis de Elementos Finitos (FEA), y se proporciona una descripción general de los estudios clÃnicos y biomecánicos sobre la optimización del ángulo de corrección de la cirugÃa de rodilla posoperatoria. Nota de contenido: Introduction -- 1. Finite element models of the knee joint -- 1.1 Knee joint models geometries -- 1.2 Material properties of hard and soft tissues -- 1.2.1 Material properties of articular cartilage -- 1.2.2 Material properties of menisci -- 1.2.3 Material properties of ligaments -- 1.2.4 Material properties of bony structure -- 2. Finite Element Analysis applications in knee joint biomechanical studies -- 2.1 Current FEA applications on ligament injury -- 2.2 Current FEA applications on meniscus injury -- 2.3 Current FEA applications on knee joint contact analysis and cartilage disease -- 3. Overview of high tibial osteotomy and optimization of correction angle -- 3.1 High tibial osteotomy definition -- 3.2 Current FEA studies on HTO procedure -- 3.3 Current clinical studies on optimizing correction angle -- 3.4 Current biomechanical studies on optimizing correction angle -- 4. Conclusions and future work -- References. Tipo de medio : Computadora Summary : In recent years, numerous scientific investigations have studied the anatomical, biomechanical and functional role of structures involved in the human knee joint. The Finite Element Method (FEM) has been seen as an interesting tool to study and simulate biosystems. It has been extensively used to analyse the knee joint and various types of knee diseases and rehabilitation procedures such as the High Tibial Osteotomy (HTO). This work presents a review on FEM analysis of the human knee joint and HTO knee surgery, and discusses how adequate this computational tool is for this type of biomedical applications. Hence, various studies addressing the knee joint based on Finite Element Analysis (FEA) are reviewed, and an overview of clinical and biomechanical studies on the optimization of the correction angle of the postoperative knee surgery is provided. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
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