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Autor Papa, João Paulo |
Documentos disponibles escritos por este autor (3)
Crear una solicitud de compra Refinar búsqueda25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers / Tavares, João Manuel R. S. ; Papa, João Paulo ; González Hidalgo, Manuel
TÃtulo : 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers Tipo de documento: documento electrónico Autores: Tavares, João Manuel R. S., ; Papa, João Paulo, ; González Hidalgo, Manuel, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XV, 490 p. 167 ilustraciones, 135 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-93420-0 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Sistemas de reconocimiento de patrones Aprendizaje automático Visión por computador IngenierÃa Informática Red de computadoras Software de la aplicacion Reconocimiento de patrones automatizado IngenierÃa Informática y Redes Aplicaciones informáticas y de sistemas de información Clasificación: Resumen: Este libro constituye las actas del 25º Congreso Iberoamericano sobre Progresos en Reconocimiento de Patrones, Análisis de Imágenes, Visión por Computador y Aplicaciones, CIARP 2021, que tuvo lugar del 10 al 13 de mayo de 2021. Inicialmente se planeó que la conferencia se llevara a cabo en Oporto, Portugal, pero cambió a un evento virtual debido a la pandemia de COVID-19. Los 45 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 82 presentaciones. Estaban organizados en secciones temáticas de la siguiente manera: aplicaciones médicas; procesamiento natural del lenguaje; metaheurÃsticas; Segmentación de imagen; bases de datos; aprendizaje profundo; inteligencia artificial explicable; procesamiento de imágenes; aprendizaje automático; y visión por computadora. . Nota de contenido: Medical Applications -- Predicting the use of Invasive Mechanical Ventilation in ICU COVID-19 patients -- A Coarse to Fine Corneal Ulcer Segmentation Approach using U-net and DexiNed in Chain -- Replacing Data Augmentation with Rotation-equivariant CNNs in Image-based Classification of Oral Cancer -- A Multitasking Learning Framework for Dermoscopic Image Analysis -- An Evaluation of Segmentation Techniques for COVID-19 Identification in Chest X-Ray -- A Study on Annotation Efficient Learning Methods for Segmentation in Prostate Histopathological Images -- Natural Language Processing -- Data-Augmented Emoji Approach to Sentiment Classification of Tweets -- Detecting Hate Speech in Cross-Lingual and Multi-Lingual Settings Using Language Agnostic Representations -- Prediction of Perception of Security Using Social Media Content -- Metaheuristics -- Fine-Tuning Dropout Regularization in Energy-Based Deep Learning -- Enhancing Hyper-To-Real Space Projections Through Euclidean NormMeta-Heuristic Optimization -- Using Particle Swarm Optimization With Gradient Descent For Parameter Learning In Convolutional Neural Networks -- Image Segmentation -- Object Delineation by Iterative Dynamic Trees -- Low-Cost Domain Adaptation for Crop and Weed Segmentation -- Databases -- MIGMA: The Facial Emotion Image Dataset forHuman Expression Recognition -- Construction of Brazilian Regulatory Traffic Sign Recognition Dataset -- Japanese Kana and Brazilian Portuguese manuscript database -- Skelibras: a Large 2d Skeleton Dataset of Dynamic Brazilian Signs -- Deep Learning -- Cricket Scene Analysis using the RetinaNet architecture. -Texture-Based Image Transformations for Improved Deep Learning Classification -- Towards Precise Recognition of Pollen Bearing Bees by Convolutional Neural Networks -- Web Application Attacks Detection Using Deep Learning -- Less is More: Accelerating Faster Neural Networks Straight from JPEG -- Optimizing Person Re-Identification using GeneratedAttention Masks -- Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing -- Explainable Artificial Intelligence -- Interpretable Concept Drift -- Interpreting a Conditional Generative Adversarial Network Model for Crime Prediction -- Interpreting Decision Patterns in Financial Applications -- Image Processing -- Metal Artifact Reduction based on color mapping and inpainting techniques -- New Improvement in Obtaining Monogenic Phase Congruency -- Machine Learning -- Evaluating the Construction of Feature Descriptors in the Performance of the Image Data Stream Classification -- Clustering-based Partitioning of Water Distribution Networks for Leak Zone Location -- Bias Quantification for Protected Features in Pattern Classification Problems -- Regional Commodities Price Volatility Assessment Using Self-Driven Recurrent Networks -- Semi-supervised Deep Learning Based on Label Propagation in a 2D Embedded Space -- Iterative Creation of Matching-Graphs - Finding Relevant Substructures in Graph Sets -- Semi-Autogeonous (SAG) Mill Overload Forecasting -- Novel Time-Frequency Based Scheme for Detecting Sound Events from Sound Background in Audio Segments -- Computer Vision -- Generalized Conics with the Sharp Corners -- Automatic Face Mask Detection using a Hide and Seek Algorithm -- A Feature Extraction Approach Based on LBP Operator and Complex Networks for Face Recognition -- End-to-End Deep Sketch-to-Photo Matching Enforcing Realistic Photo Generation -- Forensic Analysis of Tampered Digital Photos -- COVID-19 Lung CT Images Recognition: A Feature-Based Approach -- A Topologically Consistent Color Digital Image Representation by a Single Tree. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers [documento electrónico] / Tavares, João Manuel R. S., ; Papa, João Paulo, ; González Hidalgo, Manuel, . - 1 ed. . - [s.l.] : Springer, 2021 . - XV, 490 p. 167 ilustraciones, 135 ilustraciones en color.
ISBN : 978-3-030-93420-0
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Sistemas de reconocimiento de patrones Aprendizaje automático Visión por computador IngenierÃa Informática Red de computadoras Software de la aplicacion Reconocimiento de patrones automatizado IngenierÃa Informática y Redes Aplicaciones informáticas y de sistemas de información Clasificación: Resumen: Este libro constituye las actas del 25º Congreso Iberoamericano sobre Progresos en Reconocimiento de Patrones, Análisis de Imágenes, Visión por Computador y Aplicaciones, CIARP 2021, que tuvo lugar del 10 al 13 de mayo de 2021. Inicialmente se planeó que la conferencia se llevara a cabo en Oporto, Portugal, pero cambió a un evento virtual debido a la pandemia de COVID-19. Los 45 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 82 presentaciones. Estaban organizados en secciones temáticas de la siguiente manera: aplicaciones médicas; procesamiento natural del lenguaje; metaheurÃsticas; Segmentación de imagen; bases de datos; aprendizaje profundo; inteligencia artificial explicable; procesamiento de imágenes; aprendizaje automático; y visión por computadora. . Nota de contenido: Medical Applications -- Predicting the use of Invasive Mechanical Ventilation in ICU COVID-19 patients -- A Coarse to Fine Corneal Ulcer Segmentation Approach using U-net and DexiNed in Chain -- Replacing Data Augmentation with Rotation-equivariant CNNs in Image-based Classification of Oral Cancer -- A Multitasking Learning Framework for Dermoscopic Image Analysis -- An Evaluation of Segmentation Techniques for COVID-19 Identification in Chest X-Ray -- A Study on Annotation Efficient Learning Methods for Segmentation in Prostate Histopathological Images -- Natural Language Processing -- Data-Augmented Emoji Approach to Sentiment Classification of Tweets -- Detecting Hate Speech in Cross-Lingual and Multi-Lingual Settings Using Language Agnostic Representations -- Prediction of Perception of Security Using Social Media Content -- Metaheuristics -- Fine-Tuning Dropout Regularization in Energy-Based Deep Learning -- Enhancing Hyper-To-Real Space Projections Through Euclidean NormMeta-Heuristic Optimization -- Using Particle Swarm Optimization With Gradient Descent For Parameter Learning In Convolutional Neural Networks -- Image Segmentation -- Object Delineation by Iterative Dynamic Trees -- Low-Cost Domain Adaptation for Crop and Weed Segmentation -- Databases -- MIGMA: The Facial Emotion Image Dataset forHuman Expression Recognition -- Construction of Brazilian Regulatory Traffic Sign Recognition Dataset -- Japanese Kana and Brazilian Portuguese manuscript database -- Skelibras: a Large 2d Skeleton Dataset of Dynamic Brazilian Signs -- Deep Learning -- Cricket Scene Analysis using the RetinaNet architecture. -Texture-Based Image Transformations for Improved Deep Learning Classification -- Towards Precise Recognition of Pollen Bearing Bees by Convolutional Neural Networks -- Web Application Attacks Detection Using Deep Learning -- Less is More: Accelerating Faster Neural Networks Straight from JPEG -- Optimizing Person Re-Identification using GeneratedAttention Masks -- Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing -- Explainable Artificial Intelligence -- Interpretable Concept Drift -- Interpreting a Conditional Generative Adversarial Network Model for Crime Prediction -- Interpreting Decision Patterns in Financial Applications -- Image Processing -- Metal Artifact Reduction based on color mapping and inpainting techniques -- New Improvement in Obtaining Monogenic Phase Congruency -- Machine Learning -- Evaluating the Construction of Feature Descriptors in the Performance of the Image Data Stream Classification -- Clustering-based Partitioning of Water Distribution Networks for Leak Zone Location -- Bias Quantification for Protected Features in Pattern Classification Problems -- Regional Commodities Price Volatility Assessment Using Self-Driven Recurrent Networks -- Semi-supervised Deep Learning Based on Label Propagation in a 2D Embedded Space -- Iterative Creation of Matching-Graphs - Finding Relevant Substructures in Graph Sets -- Semi-Autogeonous (SAG) Mill Overload Forecasting -- Novel Time-Frequency Based Scheme for Detecting Sound Events from Sound Background in Audio Segments -- Computer Vision -- Generalized Conics with the Sharp Corners -- Automatic Face Mask Detection using a Hide and Seek Algorithm -- A Feature Extraction Approach Based on LBP Operator and Complex Networks for Face Recognition -- End-to-End Deep Sketch-to-Photo Matching Enforcing Realistic Photo Generation -- Forensic Analysis of Tampered Digital Photos -- COVID-19 Lung CT Images Recognition: A Feature-Based Approach -- A Topologically Consistent Color Digital Image Representation by a Single Tree. 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. 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: 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. 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.
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: 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. 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. Palabras clave: Visión por computador Inteligencia artificial Informática Médica Bioinformática diseño lógico Informática de la Salud BiologÃa Computacional y de Sistemas Clasificación: 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. 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.
Palabras clave: Visión por computador Inteligencia artificial Informática Médica Bioinformática diseño lógico Informática de la Salud BiologÃa Computacional y de Sistemas Clasificación: 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. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
