Computational 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  

Público ISBD Ningún comentario para este título. Título : 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. 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 Estadística en Informática  Reconocimiento de patrones automatizado Clasificación: Resumen: Este libro constituye las actas conjuntas arbitradas del Primer Taller Internacional sobre Patología 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 artículos completos (de 25 envíos) presentados en COMPAY 2018 y los 21 artículos completos (de 31 envíos) presentados en OMIA 2018 fueron cuidadosamente revisados ​​y seleccionados. Los artículos de COMPAY se centran en la inteligencia artificial y el aprendizaje profundo. Los artículos 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. 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. 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 Estadística en Informática  Reconocimiento de patrones automatizado Clasificación: Resumen: Este libro constituye las actas conjuntas arbitradas del Primer Taller Internacional sobre Patología 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 artículos completos (de 25 envíos) presentados en COMPAY 2018 y los 21 artículos completos (de 31 envíos) presentados en OMIA 2018 fueron cuidadosamente revisados ​​y seleccionados. Los artículos de COMPAY se centran en la inteligencia artificial y el aprendizaje profundo. Los artículos 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. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]

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