Información del autor
Autor Taylor, Zeike |
Documentos disponibles escritos por este autor (8)
Crear una solicitud de compra Refinar búsquedaComputational Pathology and Ophthalmic Medical Image Analysis / Stoyanov, Danail ; Taylor, Zeike ; Ciompi, Francesco ; Xu, Yanwu ; Martel, Anne ; Maier-Hein, Lena ; Rajpoot, Nasir ; van der Laak, Jeroen ; Veta, Mitko ; McKenna, Stephen ; Snead, David ; Trucco, Emanuele ; Garvin, Mona K. ; Chen, Xin Jan ; Bogunovic, Hrvoje
TÃtulo : Computational Pathology and Ophthalmic Medical Image Analysis : First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 - 20, 2018, Proceedings Tipo de documento: documento electrónico Autores: Stoyanov, Danail, ; Taylor, Zeike, ; Ciompi, Francesco, ; Xu, Yanwu, ; Martel, Anne, ; Maier-Hein, Lena, ; Rajpoot, Nasir, ; van der Laak, Jeroen, ; Veta, Mitko, ; McKenna, Stephen, ; Snead, David, ; Trucco, Emanuele, ; Garvin, Mona K., ; Chen, Xin Jan, ; Bogunovic, Hrvoje, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XVII, 347 p. 135 ilustraciones ISBN/ISSN/DL: 978-3-030-00949-6 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 Unidades aritméticas y lógicas informáticas. Informática Estadistica matematica Sistemas de reconocimiento de patrones Estructuras aritméticas y lógicas Probabilidad y EstadÃstica en Informática Reconocimiento de patrones automatizado Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Primer Taller Internacional sobre PatologÃa Computacional, COMPAY 2018, y el 5º Taller Internacional sobre Análisis de Imágenes Médicas Oftálmicas, OMIA 2018, celebrado junto con la 21ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora. MICCAI 2018, en Granada, España, en septiembre de 2018. Los 19 artÃculos completos (de 25 envÃos) presentados en COMPAY 2018 y los 21 artÃculos completos (de 31 envÃos) presentados en OMIA 2018 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de COMPAY se centran en la inteligencia artificial y el aprendizaje profundo. Los artÃculos de OMIA cubren diversos temas en el campo del análisis de imágenes oftálmicas. Nota de contenido: Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability -- Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images -- Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network -- Construction of a Generative Model of H&E Stained Pathology Images of Pancreas Tumors Conditioned by a Voxel Value of MRI Image -- Accurate 3D reconstruction of a whole pancreatic cancer tumor from pathology images with different stains -- Role of Task Complexity and Training in Crowdsourced Image Annotation -- Capturing global spatial context for accurate cell classification in skin cancer histology -- Exploiting Multiple Color Representations to Improve Colon Cancer Detection in Whole Slide H&E Stains -- Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection -- Evaluating Out-of-the-box Methods for the Classification of Hematopoietic Cells in Images of Stained Bone Marrow -- DeepCerv: Deep neural network for segmentation free robustcervical cell classification -- Whole slide image registration for the study of tumor heterogeneity -- Modality Conversion from Pathological Image to Ultrasonic Image Using Convolutional Neural Network -- Structure instance segmentation in renal tissue: a case study on tubular immune cell detection -- Cellular Community Detection for Tissue Phenotyping in Histology Images -- Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning -- Significance of Hyperparameter Optimization for Metastasis Detection in Breast Histology Images -- Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content -- Uncertainty Driven Pooling Network for Microvessel Segmentation in Routine Histology Images -- Ocular Structures Segmentation from Multi-sequences MRI using 3D Unet with Fully Connected CRFs -- Classification of Findings with Localized Lesions in Fundoscopic Images using a Regionally Guided CNN -- Segmentation of Corneal Nerves Using a U-Net-based Convolutional Neural Network -- Automatic Pigmentation Grading of the Trabecular Meshwork in Gonioscopic Images -- Large Receptive Field Fully Convolutional Network for Semantic Segmentation of Retinal Vasculature in Fundus Images -- Explaining Convolutional Neural Networks for Area Estimation of Choroidal Neovascularization via Genetic Programming -- Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning -- cGAN-based lacquer cracks segmentation in ICGA image -- Localizing Optic Disc and Cup for Glaucoma Screening via Deep Object Detection Networks -- Fundus Image Quality-guided Diabetic Retinopathy Grading -- DeepDisc: Optic Disc Segmentation based on Atrous Convolution and Spatial Pyramid Pooling -- Large-scale Left and Right Eye Classification in Retinal Images -- Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images -- Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography -- Visual Field based Automatic Diagnosis of Glaucoma Using Deep Convolutional Neural Network -- Towards standardization of retinal vascular measurements: on the effect of image centering -- Feasibility study of Subfoveal Choroidal Thickness Changes in Spectral-Domain Optical Coherence Tomography Measurements of Macular Telangiectasia Type 2 -- Segmentation of retinal layers in OCT images of the mouse eye utilizing polarization contrast -- Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation -- 2D Modeling and Correction of Fan-beam Scan Geometry in OCT -- A Bottom-up Saliency Estimation Approach for Neonatal Retinal Images. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the First International Workshop on Computational Pathology, COMPAY 2018, and the 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 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 19 full papers (out of 25 submissions) presented at COMPAY 2018 and the 21 full papers (out of 31 submissions) presented at OMIA 2018 were carefully reviewed and selected. The COMPAY papers focus on artificial intelligence and deep learning. The OMIA papers cover various topics in the field of ophthalmic image analysis. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Computational Pathology and Ophthalmic Medical Image Analysis : First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 - 20, 2018, Proceedings [documento electrónico] / Stoyanov, Danail, ; Taylor, Zeike, ; Ciompi, Francesco, ; Xu, Yanwu, ; Martel, Anne, ; Maier-Hein, Lena, ; Rajpoot, Nasir, ; van der Laak, Jeroen, ; Veta, Mitko, ; McKenna, Stephen, ; Snead, David, ; Trucco, Emanuele, ; Garvin, Mona K., ; Chen, Xin Jan, ; Bogunovic, Hrvoje, . - 1 ed. . - [s.l.] : Springer, 2018 . - XVII, 347 p. 135 ilustraciones.
ISBN : 978-3-030-00949-6
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 Unidades aritméticas y lógicas informáticas. Informática Estadistica matematica Sistemas de reconocimiento de patrones Estructuras aritméticas y lógicas Probabilidad y EstadÃstica en Informática Reconocimiento de patrones automatizado Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Primer Taller Internacional sobre PatologÃa Computacional, COMPAY 2018, y el 5º Taller Internacional sobre Análisis de Imágenes Médicas Oftálmicas, OMIA 2018, celebrado junto con la 21ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora. MICCAI 2018, en Granada, España, en septiembre de 2018. Los 19 artÃculos completos (de 25 envÃos) presentados en COMPAY 2018 y los 21 artÃculos completos (de 31 envÃos) presentados en OMIA 2018 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de COMPAY se centran en la inteligencia artificial y el aprendizaje profundo. Los artÃculos de OMIA cubren diversos temas en el campo del análisis de imágenes oftálmicas. Nota de contenido: Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability -- Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images -- Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network -- Construction of a Generative Model of H&E Stained Pathology Images of Pancreas Tumors Conditioned by a Voxel Value of MRI Image -- Accurate 3D reconstruction of a whole pancreatic cancer tumor from pathology images with different stains -- Role of Task Complexity and Training in Crowdsourced Image Annotation -- Capturing global spatial context for accurate cell classification in skin cancer histology -- Exploiting Multiple Color Representations to Improve Colon Cancer Detection in Whole Slide H&E Stains -- Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection -- Evaluating Out-of-the-box Methods for the Classification of Hematopoietic Cells in Images of Stained Bone Marrow -- DeepCerv: Deep neural network for segmentation free robustcervical cell classification -- Whole slide image registration for the study of tumor heterogeneity -- Modality Conversion from Pathological Image to Ultrasonic Image Using Convolutional Neural Network -- Structure instance segmentation in renal tissue: a case study on tubular immune cell detection -- Cellular Community Detection for Tissue Phenotyping in Histology Images -- Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning -- Significance of Hyperparameter Optimization for Metastasis Detection in Breast Histology Images -- Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content -- Uncertainty Driven Pooling Network for Microvessel Segmentation in Routine Histology Images -- Ocular Structures Segmentation from Multi-sequences MRI using 3D Unet with Fully Connected CRFs -- Classification of Findings with Localized Lesions in Fundoscopic Images using a Regionally Guided CNN -- Segmentation of Corneal Nerves Using a U-Net-based Convolutional Neural Network -- Automatic Pigmentation Grading of the Trabecular Meshwork in Gonioscopic Images -- Large Receptive Field Fully Convolutional Network for Semantic Segmentation of Retinal Vasculature in Fundus Images -- Explaining Convolutional Neural Networks for Area Estimation of Choroidal Neovascularization via Genetic Programming -- Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning -- cGAN-based lacquer cracks segmentation in ICGA image -- Localizing Optic Disc and Cup for Glaucoma Screening via Deep Object Detection Networks -- Fundus Image Quality-guided Diabetic Retinopathy Grading -- DeepDisc: Optic Disc Segmentation based on Atrous Convolution and Spatial Pyramid Pooling -- Large-scale Left and Right Eye Classification in Retinal Images -- Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images -- Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography -- Visual Field based Automatic Diagnosis of Glaucoma Using Deep Convolutional Neural Network -- Towards standardization of retinal vascular measurements: on the effect of image centering -- Feasibility study of Subfoveal Choroidal Thickness Changes in Spectral-Domain Optical Coherence Tomography Measurements of Macular Telangiectasia Type 2 -- Segmentation of retinal layers in OCT images of the mouse eye utilizing polarization contrast -- Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation -- 2D Modeling and Correction of Fan-beam Scan Geometry in OCT -- A Bottom-up Saliency Estimation Approach for Neonatal Retinal Images. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the First International Workshop on Computational Pathology, COMPAY 2018, and the 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 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 19 full papers (out of 25 submissions) presented at COMPAY 2018 and the 21 full papers (out of 31 submissions) presented at OMIA 2018 were carefully reviewed and selected. The COMPAY papers focus on artificial intelligence and deep learning. The OMIA papers cover various topics in the field of ophthalmic image analysis. 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 : Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities : Second International Workshop, GRAIL 2018 and First International Workshop, Beyond MIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings Tipo de documento: documento electrónico Autores: Stoyanov, Danail, ; Taylor, Zeike, ; Ferrante, Enzo, ; Dalca, Adrian V., ; Martel, Anne, ; Maier-Hein, Lena, ; Parisot, Sarah, ; Sotiras, Aristeidis, ; Papiez, Bartlomiej, ; Sabuncu, Mert R., ; Shen, Li, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XVI, 101 p. 26 ilustraciones ISBN/ISSN/DL: 978-3-030-00689-1 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: Estructuras de datos (Informática) Estructuras de datos y teorÃa de la información Inteligencia artificial Matemáticas de la Computación Visión por computador TeorÃa de la información Algoritmo Matemáticas Informática Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Segundo Taller Internacional sobre Gráficos en Análisis de Imágenes Biomédicas, GRAIL 2018 y el Primer Taller Internacional sobre la Integración de Modalidades de Imágenes Médicas y No Imágenes, Más Allá de MIC 2018, celebrado junto con la 21ª Conferencia Internacional sobre Medicina. Imaging and Computer-Assisted Intervention, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 6 artÃculos completos presentados en GRAIL 2018 y los 5 artÃculos completos presentados en BeYond MIC 2018 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de GRAIL cubren una amplia gama de modelos basados ​​en gráficos para el análisis de imágenes biomédicas y fomentan la exploración de modelos basados ​​en gráficos para problemas clÃnicos difÃciles dentro de una variedad de contextos de imágenes biomédicas. Los artÃculos de Beyond MIC cubren temas de métodos novedosos con importantes componentes de imágenes y no imágenes, abordando aplicaciones prácticas y nuevos conjuntos de datos. Nota de contenido: Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity -- A Graph Representation and Similarity Measure for Brain Networks with Nodal Features -- Hierarchical Bayesian Networks for Modeling Inter-Class Dependencies: Application to Semi-Supervised Cell Segmentation -- Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion -- BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classification -- Modeling Brain Networks with Artificial Neural Networks -- A Bayesian Disease Progression Model for Clinical Trajectories -- Multi-modal brain connectivity study using deep collaborative learning -- Towards Subject and Diagnostic Identifiability in the Alzheimer's Disease Spectrum based on Functional Connectomes -- Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging -- Cross-Diagnostic Prediction of Dimensional Psychiatric Phenotypes inAnorexia Nervosa and Body Dysmorphic Disorder Using Multimodal Neuroimaging and Psychometric Data. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Second International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and the First International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 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 6 full papers presented at GRAIL 2018 and the 5 full papers presented at BeYond MIC 2018 were carefully reviewed and selected. The GRAIL papers cover a wide range of develop graph-based models for the analysis of biomedical images and encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. The Beyond MIC papers cover topics of novel methods with significant imaging and non-imaging components, addressing practical applications and new datasets. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities : Second International Workshop, GRAIL 2018 and First International Workshop, Beyond MIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings [documento electrónico] / Stoyanov, Danail, ; Taylor, Zeike, ; Ferrante, Enzo, ; Dalca, Adrian V., ; Martel, Anne, ; Maier-Hein, Lena, ; Parisot, Sarah, ; Sotiras, Aristeidis, ; Papiez, Bartlomiej, ; Sabuncu, Mert R., ; Shen, Li, . - 1 ed. . - [s.l.] : Springer, 2018 . - XVI, 101 p. 26 ilustraciones.
ISBN : 978-3-030-00689-1
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: Estructuras de datos (Informática) Estructuras de datos y teorÃa de la información Inteligencia artificial Matemáticas de la Computación Visión por computador TeorÃa de la información Algoritmo Matemáticas Informática Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Segundo Taller Internacional sobre Gráficos en Análisis de Imágenes Biomédicas, GRAIL 2018 y el Primer Taller Internacional sobre la Integración de Modalidades de Imágenes Médicas y No Imágenes, Más Allá de MIC 2018, celebrado junto con la 21ª Conferencia Internacional sobre Medicina. Imaging and Computer-Assisted Intervention, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 6 artÃculos completos presentados en GRAIL 2018 y los 5 artÃculos completos presentados en BeYond MIC 2018 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de GRAIL cubren una amplia gama de modelos basados ​​en gráficos para el análisis de imágenes biomédicas y fomentan la exploración de modelos basados ​​en gráficos para problemas clÃnicos difÃciles dentro de una variedad de contextos de imágenes biomédicas. Los artÃculos de Beyond MIC cubren temas de métodos novedosos con importantes componentes de imágenes y no imágenes, abordando aplicaciones prácticas y nuevos conjuntos de datos. Nota de contenido: Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity -- A Graph Representation and Similarity Measure for Brain Networks with Nodal Features -- Hierarchical Bayesian Networks for Modeling Inter-Class Dependencies: Application to Semi-Supervised Cell Segmentation -- Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion -- BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classification -- Modeling Brain Networks with Artificial Neural Networks -- A Bayesian Disease Progression Model for Clinical Trajectories -- Multi-modal brain connectivity study using deep collaborative learning -- Towards Subject and Diagnostic Identifiability in the Alzheimer's Disease Spectrum based on Functional Connectomes -- Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging -- Cross-Diagnostic Prediction of Dimensional Psychiatric Phenotypes inAnorexia Nervosa and Body Dysmorphic Disorder Using Multimodal Neuroimaging and Psychometric Data. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Second International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and the First International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 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 6 full papers presented at GRAIL 2018 and the 5 full papers presented at BeYond MIC 2018 were carefully reviewed and selected. The GRAIL papers cover a wide range of develop graph-based models for the analysis of biomedical images and encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. The Beyond MIC papers cover topics of novel methods with significant imaging and non-imaging components, addressing practical applications and new datasets. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Image Analysis for Moving Organ, Breast, and Thoracic Images : Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings Tipo de documento: documento electrónico Autores: Stoyanov, Danail, ; Taylor, Zeike, ; Kainz, Bernhard, ; Maicas, Gabriel, ; Beichel, Reinhard R., ; Martel, Anne, ; Maier-Hein, Lena, ; Bhatia, Kanwal, ; Vercauteren, Tom, ; Oktay, Ozan, ; Carneiro, Gustavo, ; Bradley, Andrew P., ; Nascimento, Jacinto, ; Min, Hang, ; Brown, Matthew S., ; Jacobs, Colin, ; Lassen-Schmidt, Bianca, ; Mori, Kensaku, ; Petersen, Jens, ; San José Estépar, Raúl, ; Schmidt-Richberg, Alexander, ; Veiga, Catarina, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XIV, 350 p. 144 ilustraciones ISBN/ISSN/DL: 978-3-030-00946-5 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: Visión por computador Informática de la Salud Inteligencia artificial Informática Médica Redes de comunicación informática Red informática Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Tercer Taller Internacional sobre Reconstrucción y Análisis de Órganos del Cuerpo en Movimiento, RAMBO 2018, el Cuarto Taller Internacional sobre Análisis de Imágenes Mamarias, BIA 2018, y el Primer Taller Internacional sobre Análisis de Imágenes Torácicas, TIA 2018, celebrado en conjunto con la 21.a Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 5 artÃculos completos (de 10 presentaciones) presentados en RAMBO, los 9 artÃculos completos (de 18 presentaciones) presentadas en BIA, y los 20 artÃculos completos (de 21 presentaciones) presentados en TIA fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de RAMBO cubren aspectos de las imágenes médicas en los que el movimiento desempeña un papel en la formación o el análisis de las imágenes. Los artÃculos de la BIA tratan temas como la detección y el diagnóstico del cáncer de mama asistido por computadora, el análisis cuantitativo de las modalidades de imágenes de la mama y la detección y el análisis de imágenes de la mama a gran escala. Los artÃculos de la TIA cubren aspectos de la investigación del análisis de imágenes para enfermedades pulmonares y cardÃacas, incluida la segmentación, el registro, la cuantificación, el modelado del proceso de adquisición de imágenes, la visualización, la validación, el modelado estadÃstico, el modelado biofÃsico del pulmón (anatomÃa computacional), el aprendizaje profundo y aplicaciones novedosas. Nota de contenido: Resection-based Demons Regularization for Breast Tumor Bed Propagation -- Linear and Deformable Image Registration with 3D Convolutional Neural Networks -- Super Resolution of Cardiac Cine MRI Sequences Using Deep Learning -- Automated CNN-based Reconstruction of Short-Axis Cardiac MR Sequence From Real-Time Image Data -- An Unbiased Groupwise Registration Algorithm for Correcting Motion in Dynamic Contrast-Enhanced Magnetic Resonance Images -- Siamese Network for Dual-View Mammography Mass Matching -- Large-scale Mammography CAD with Deformable Conv-Nets -- Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images -- Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net -- Improving Breast Cancer Detection using Symmetry Information -- Conditional Infilling GANs for Data Augmentation in Mammogram Classification -- A Unified Mammogram Analysis Method via Hybrid Deep Supervision -- Structure-aware Staging for Breast Cancer Metastases -- Reproducible evaluation of registration algorithms for movement correction in dynamic contrast enhancing magnetic resonance imaging for breast cancer diagnosis -- Robust Windowed Harmonic Phase Analysis with a Single Acquisition -- Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training -- Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder -- Tuberculosis histopathology on x-ray CT -- A CT scan harmonization technique to detect Emphysema and Small Airway Diseases -- Transfer Learning for Segmentation of Injured Lungs using Coarse-to-Fine Convolutional Neural Networks -- High throughput lung and lobar segmentation by 2D and 3D CNN on chest CT with diffuse lung disease -- Multi-Structure Segmentation from Partially Labeled Datasets. Application to Body Composition Measurements on CT scans -- 3D Pulmonary Artery Segmentation from CTA Scans using Deep Learning with Realistic Data Augmentation -- Automatic Airway Segmentation in chest CT using Convolutional Neural Networks -- Detecting Out-of-phase Ventilation Using 4DCT to Improve Radiation Therapy for Lung Cancer -- XeMRI to CT Lung Image Registration Enhanced with Personalized 4DCT-derived Motion Model -- Rigid Lens – Locally Rigid Approximations of Deformable Registration for Change Assessment in Thorax-Abdomen CT Follow-Up Scan -- Diffeomorphic Lung Registration using Deep CNNs and Reinforced Learning -- Transfer learning approach to predict biopsy-confirmed malignancy of lung nodules from imaging data: a pilot study -- Convolutional Neural Network Based COPD and Emphysema Classifications Are Predictive of Lung Cancer Diagnosis -- Towards an automatic lung cancer screening system in low dose computed tomography -- Automatic classification of centrilobular emphysema on CT using deep learning: comparison with visual scoring -- On the Relevance of the Loss Function in the Agatston Score Regression from non-ECG Gated CT Scans -- Accurate Measurement of Airway Morphology on Chest CT images. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Third International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, the Fourth International Workshop on Breast Image Analysis, BIA 2018, and the First International Workshop on Thoracic Image Analysis, TIA 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 5 full papers (out of 10 submissions) presented at RAMBO, the 9 full papers (out of 18 submissions) presented at BIA, and the 20 full papers (out of 21 submissions) presented at TIA were carefully reviewed and selected. The RAMBO papers cover aspects of medical imaging where motion plays a role in the image formation or analysis. The BIA papers deal with topics such as computer-aided detection and diagnosis of breast cancer, quantitative analysis of breast imaging modalities, and large scale breast image screening and analysis. The TIA papers cover aspects of image analysis research for lung and cardiac diseases including segmentation, registration, quantification, modeling of the image acquisition process, visualization, validation, statistical modeling, biophysical lung modeling (computational anatomy), deep learning and novel applications. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Image Analysis for Moving Organ, Breast, and Thoracic Images : Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings [documento electrónico] / Stoyanov, Danail, ; Taylor, Zeike, ; Kainz, Bernhard, ; Maicas, Gabriel, ; Beichel, Reinhard R., ; Martel, Anne, ; Maier-Hein, Lena, ; Bhatia, Kanwal, ; Vercauteren, Tom, ; Oktay, Ozan, ; Carneiro, Gustavo, ; Bradley, Andrew P., ; Nascimento, Jacinto, ; Min, Hang, ; Brown, Matthew S., ; Jacobs, Colin, ; Lassen-Schmidt, Bianca, ; Mori, Kensaku, ; Petersen, Jens, ; San José Estépar, Raúl, ; Schmidt-Richberg, Alexander, ; Veiga, Catarina, . - 1 ed. . - [s.l.] : Springer, 2018 . - XIV, 350 p. 144 ilustraciones.
ISBN : 978-3-030-00946-5
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: Visión por computador Informática de la Salud Inteligencia artificial Informática Médica Redes de comunicación informática Red informática Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Tercer Taller Internacional sobre Reconstrucción y Análisis de Órganos del Cuerpo en Movimiento, RAMBO 2018, el Cuarto Taller Internacional sobre Análisis de Imágenes Mamarias, BIA 2018, y el Primer Taller Internacional sobre Análisis de Imágenes Torácicas, TIA 2018, celebrado en conjunto con la 21.a Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 5 artÃculos completos (de 10 presentaciones) presentados en RAMBO, los 9 artÃculos completos (de 18 presentaciones) presentadas en BIA, y los 20 artÃculos completos (de 21 presentaciones) presentados en TIA fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de RAMBO cubren aspectos de las imágenes médicas en los que el movimiento desempeña un papel en la formación o el análisis de las imágenes. Los artÃculos de la BIA tratan temas como la detección y el diagnóstico del cáncer de mama asistido por computadora, el análisis cuantitativo de las modalidades de imágenes de la mama y la detección y el análisis de imágenes de la mama a gran escala. Los artÃculos de la TIA cubren aspectos de la investigación del análisis de imágenes para enfermedades pulmonares y cardÃacas, incluida la segmentación, el registro, la cuantificación, el modelado del proceso de adquisición de imágenes, la visualización, la validación, el modelado estadÃstico, el modelado biofÃsico del pulmón (anatomÃa computacional), el aprendizaje profundo y aplicaciones novedosas. Nota de contenido: Resection-based Demons Regularization for Breast Tumor Bed Propagation -- Linear and Deformable Image Registration with 3D Convolutional Neural Networks -- Super Resolution of Cardiac Cine MRI Sequences Using Deep Learning -- Automated CNN-based Reconstruction of Short-Axis Cardiac MR Sequence From Real-Time Image Data -- An Unbiased Groupwise Registration Algorithm for Correcting Motion in Dynamic Contrast-Enhanced Magnetic Resonance Images -- Siamese Network for Dual-View Mammography Mass Matching -- Large-scale Mammography CAD with Deformable Conv-Nets -- Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images -- Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net -- Improving Breast Cancer Detection using Symmetry Information -- Conditional Infilling GANs for Data Augmentation in Mammogram Classification -- A Unified Mammogram Analysis Method via Hybrid Deep Supervision -- Structure-aware Staging for Breast Cancer Metastases -- Reproducible evaluation of registration algorithms for movement correction in dynamic contrast enhancing magnetic resonance imaging for breast cancer diagnosis -- Robust Windowed Harmonic Phase Analysis with a Single Acquisition -- Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training -- Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder -- Tuberculosis histopathology on x-ray CT -- A CT scan harmonization technique to detect Emphysema and Small Airway Diseases -- Transfer Learning for Segmentation of Injured Lungs using Coarse-to-Fine Convolutional Neural Networks -- High throughput lung and lobar segmentation by 2D and 3D CNN on chest CT with diffuse lung disease -- Multi-Structure Segmentation from Partially Labeled Datasets. Application to Body Composition Measurements on CT scans -- 3D Pulmonary Artery Segmentation from CTA Scans using Deep Learning with Realistic Data Augmentation -- Automatic Airway Segmentation in chest CT using Convolutional Neural Networks -- Detecting Out-of-phase Ventilation Using 4DCT to Improve Radiation Therapy for Lung Cancer -- XeMRI to CT Lung Image Registration Enhanced with Personalized 4DCT-derived Motion Model -- Rigid Lens – Locally Rigid Approximations of Deformable Registration for Change Assessment in Thorax-Abdomen CT Follow-Up Scan -- Diffeomorphic Lung Registration using Deep CNNs and Reinforced Learning -- Transfer learning approach to predict biopsy-confirmed malignancy of lung nodules from imaging data: a pilot study -- Convolutional Neural Network Based COPD and Emphysema Classifications Are Predictive of Lung Cancer Diagnosis -- Towards an automatic lung cancer screening system in low dose computed tomography -- Automatic classification of centrilobular emphysema on CT using deep learning: comparison with visual scoring -- On the Relevance of the Loss Function in the Agatston Score Regression from non-ECG Gated CT Scans -- Accurate Measurement of Airway Morphology on Chest CT images. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Third International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, the Fourth International Workshop on Breast Image Analysis, BIA 2018, and the First International Workshop on Thoracic Image Analysis, TIA 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 5 full papers (out of 10 submissions) presented at RAMBO, the 9 full papers (out of 18 submissions) presented at BIA, and the 20 full papers (out of 21 submissions) presented at TIA were carefully reviewed and selected. The RAMBO papers cover aspects of medical imaging where motion plays a role in the image formation or analysis. The BIA papers deal with topics such as computer-aided detection and diagnosis of breast cancer, quantitative analysis of breast imaging modalities, and large scale breast image screening and analysis. The TIA papers cover aspects of image analysis research for lung and cardiac diseases including segmentation, registration, quantification, modeling of the image acquisition process, visualization, validation, statistical modeling, biophysical lung modeling (computational anatomy), deep learning and novel applications. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis : 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings Tipo de documento: documento electrónico Autores: Stoyanov, Danail, ; Taylor, Zeike, ; Balocco, Simone, ; Sznitman, Raphael, ; Martel, Anne, ; Maier-Hein, Lena, ; Duong, Luc, ; Zahnd, Guillaume, ; Demirci, Stefanie, ; Albarqouni, Shadi, ; Lee, Su-Lin, ; Moriconi, Stefano, ; Cheplygina, Veronika, ; Mateus, Diana, ; Trucco, Emanuele, ; Granger, Eric, ; Jannin, Pierre, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XVII, 202 p. 111 ilustraciones, 65 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-01364-6 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: Informática Médica Visión por computador IngenierÃa Informática y Redes Inteligencia artificial Informática de la Salud Red informática IngenierÃa Informática Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del 7.º Taller internacional conjunto sobre computación y visualización para imágenes intravasculares y colocación de stents asistida por computadora, CVII-STENT 2018, y el Tercer taller internacional sobre anotación a gran escala de datos biomédicos y sÃntesis experta de etiquetas, LABELS 2018, celebrados conjuntamente con la 21.ª Conferencia internacional sobre imágenes médicas e intervención asistida por computadora, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 9 artÃculos completos presentados en CVII-STENT 2017 y los 12 artÃculos completos presentados en LABELS 2017 fueron revisados ​​y seleccionados cuidadosamente. Los artÃculos de CVII-STENT presentan el estado del arte en imágenes, tratamiento e intervención asistida por computadora en el campo de las intervenciones endovasculares. Los artÃculos de LABELS presentan una variedad de enfoques para abordar pocas etiquetas, desde el aprendizaje por transferencia hasta el crowdsourcing. Nota de contenido: Blood-flow estimation in the hepatic arteries based on 3D/2D angiography registration -- Automated quantification of blood flow velocity from time-resolved CT angiography -- Multiple device segmentation for fluoroscopic imaging using multi-task learning -- Segmentation of the Aorta Using Active Contours with Histogram-Based Descriptors -- Layer Separation in X-ray Angiograms for Vessel Enhancement with Fully Convolutional Network -- Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts -- Deep Learning-based Detection and Segmentation for BVS Struts in IVOCT Images -- Towards Automatic Measurement of Type B Aortic Dissection Parameters -- Prediction of FFR from IVUS Images using Machine Learning -- Deep Learning Retinal Vessel Segmentation From a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks -- An Efficient and Comprehensive Labeling Tool for Large-scale Annotation of Fundus Images -- Crowd disagreement about medical images is informative -- Imperfect Segmentation Labels: How Much Do They Matter? -- Crowdsourcing annotation of surgical instruments in videos of cataract surgery -- Four-dimensional ASL MR angiography phantoms with noise learned by neural styling -- Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans -- Capsule Networks against Medical Imaging Data Challenges -- Fully Automatic Segmentation of Coronary Arteries based on Deep Neural Network in Intravascular Ultrasound Images -- Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos -- Radiology Objects in COntext (ROCO) -- Improving out-of-sample prediction of quality of MRIQC. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the Third International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 9 full papers presented at CVII-STENT 2017 and the 12 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis : 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings [documento electrónico] / Stoyanov, Danail, ; Taylor, Zeike, ; Balocco, Simone, ; Sznitman, Raphael, ; Martel, Anne, ; Maier-Hein, Lena, ; Duong, Luc, ; Zahnd, Guillaume, ; Demirci, Stefanie, ; Albarqouni, Shadi, ; Lee, Su-Lin, ; Moriconi, Stefano, ; Cheplygina, Veronika, ; Mateus, Diana, ; Trucco, Emanuele, ; Granger, Eric, ; Jannin, Pierre, . - 1 ed. . - [s.l.] : Springer, 2018 . - XVII, 202 p. 111 ilustraciones, 65 ilustraciones en color.
ISBN : 978-3-030-01364-6
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: Informática Médica Visión por computador IngenierÃa Informática y Redes Inteligencia artificial Informática de la Salud Red informática IngenierÃa Informática Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del 7.º Taller internacional conjunto sobre computación y visualización para imágenes intravasculares y colocación de stents asistida por computadora, CVII-STENT 2018, y el Tercer taller internacional sobre anotación a gran escala de datos biomédicos y sÃntesis experta de etiquetas, LABELS 2018, celebrados conjuntamente con la 21.ª Conferencia internacional sobre imágenes médicas e intervención asistida por computadora, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 9 artÃculos completos presentados en CVII-STENT 2017 y los 12 artÃculos completos presentados en LABELS 2017 fueron revisados ​​y seleccionados cuidadosamente. Los artÃculos de CVII-STENT presentan el estado del arte en imágenes, tratamiento e intervención asistida por computadora en el campo de las intervenciones endovasculares. Los artÃculos de LABELS presentan una variedad de enfoques para abordar pocas etiquetas, desde el aprendizaje por transferencia hasta el crowdsourcing. Nota de contenido: Blood-flow estimation in the hepatic arteries based on 3D/2D angiography registration -- Automated quantification of blood flow velocity from time-resolved CT angiography -- Multiple device segmentation for fluoroscopic imaging using multi-task learning -- Segmentation of the Aorta Using Active Contours with Histogram-Based Descriptors -- Layer Separation in X-ray Angiograms for Vessel Enhancement with Fully Convolutional Network -- Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts -- Deep Learning-based Detection and Segmentation for BVS Struts in IVOCT Images -- Towards Automatic Measurement of Type B Aortic Dissection Parameters -- Prediction of FFR from IVUS Images using Machine Learning -- Deep Learning Retinal Vessel Segmentation From a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks -- An Efficient and Comprehensive Labeling Tool for Large-scale Annotation of Fundus Images -- Crowd disagreement about medical images is informative -- Imperfect Segmentation Labels: How Much Do They Matter? -- Crowdsourcing annotation of surgical instruments in videos of cataract surgery -- Four-dimensional ASL MR angiography phantoms with noise learned by neural styling -- Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans -- Capsule Networks against Medical Imaging Data Challenges -- Fully Automatic Segmentation of Coronary Arteries based on Deep Neural Network in Intravascular Ultrasound Images -- Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos -- Radiology Objects in COntext (ROCO) -- Improving out-of-sample prediction of quality of MRIQC. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the Third International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 9 full papers presented at CVII-STENT 2017 and the 12 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
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