Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support / 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  

Público ISBD Ningún comentario para este título. Título : 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 clínicas, 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 artículos completos presentados en DLMIA 2018 y los 4 artículos completos presentados en ML-CDS 2018 fueron cuidadosamente revisados ​​y seleccionados entre 85 presentaciones a DLMIA y 6 presentaciones a ML-CDS. Los artículos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artículos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clínicas. 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 clínicas, 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 artículos completos presentados en DLMIA 2018 y los 4 artículos completos presentados en ML-CDS 2018 fueron cuidadosamente revisados ​​y seleccionados entre 85 presentaciones a DLMIA y 6 presentaciones a ML-CDS. Los artículos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artículos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clínicas. 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 [...]

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