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Autor Garvin, Mona K. |
Documentos disponibles escritos por este autor (4)
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. 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: 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. 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 EstadÃstica en Informática Reconocimiento de patrones automatizado Clasificación: 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. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Ophthalmic Medical Image Analysis : 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, Proceedings Tipo de documento: documento electrónico Autores: Fu, Huazhu, ; Garvin, Mona K., ; MacGillivray, Tom, ; Xu, Yanwu, ; Zheng, Yalin, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XI, 192 p. 80 ilustraciones, 78 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-32956-3 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 IngenierÃa Informática Red de computadoras Matemáticas de la Computación IngenierÃa Informática y Redes Clasificación: Resumen: Este libro constituye las actas arbitradas del 6.º Taller Internacional sobre Análisis de Imágenes Médicas Oftálmicas, OMIA 2019, celebrado junto con la 22.ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2019, en Shenzhen, China, en octubre de 2019. Se revisaron y seleccionaron cuidadosamente 22 artÃculos completos (de 36 presentaciones) presentados en OMIA 2019. Los artÃculos cubren diversos temas en el campo del análisis de imágenes oftálmicas. Nota de contenido: Dictionary Learning Informed Deep Neural Network with Application to OCT Images -- Structure-aware Noise Reduction Generative Adversarial Network for Optical Coherence Tomography Image -- Region-Based Segmentation of Capillary Density in Optical Coherence Tomography Angiography -- An ampli?b￾ed-target loss approach for photoreceptor layer segmentation in pathological OCT scans -- Foveal avascular zone segmentation in clinical routine ?b‚uorescein angiographies using multitask learning -- Guided M-Net for High-resolution Biomedical Image Segmentation with Weak Boundaries -- 3D-CNN for Glaucoma Detection using Optical Coherence Tomography -- Semi-supervised Adversarial Learning for Diabetic Retinopathy Screening -- Shape Decomposition of Foveal Pit Morphology using Scan Geometry Corrected OCT -- U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography -- Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal Images -- Robust Optic Disc Localization by Large Scale Learning -- The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detections -- Fundus Image based Retinal Vessel Segmentation Utilizing A Fast and Accurate Fully Convolutional Network -- Network pruning for OCT image classi?b￾cation -- An improved MPB-CNN segmentation method for edema area and neurosensory retinal detachment in SD-OCT images -- Encoder-Decoder Attention Network for Lesion Segmentation of Diabetic Retinopathy -- Multi-Discriminator Generative Adversarial Networks for improved thin retinal vessel segmentation -- Fovea Localization in Fundus Photographs by Faster R-CNN with Physiological Prior -- Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis via a Deep Convolutional Network -- Automated Stage Analysis of Retinopathy of Prematurity Using Joint Segmentation and Multi-Instance Learning -- Retinopathy Diagnosis using Semi-supervised Multi-channel Generative Adversarial Network. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Ophthalmic Medical Image Analysis : 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, Proceedings [documento electrónico] / Fu, Huazhu, ; Garvin, Mona K., ; MacGillivray, Tom, ; Xu, Yanwu, ; Zheng, Yalin, . - 1 ed. . - [s.l.] : Springer, 2019 . - XI, 192 p. 80 ilustraciones, 78 ilustraciones en color.
ISBN : 978-3-030-32956-3
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 IngenierÃa Informática Red de computadoras Matemáticas de la Computación IngenierÃa Informática y Redes Clasificación: Resumen: Este libro constituye las actas arbitradas del 6.º Taller Internacional sobre Análisis de Imágenes Médicas Oftálmicas, OMIA 2019, celebrado junto con la 22.ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2019, en Shenzhen, China, en octubre de 2019. Se revisaron y seleccionaron cuidadosamente 22 artÃculos completos (de 36 presentaciones) presentados en OMIA 2019. Los artÃculos cubren diversos temas en el campo del análisis de imágenes oftálmicas. Nota de contenido: Dictionary Learning Informed Deep Neural Network with Application to OCT Images -- Structure-aware Noise Reduction Generative Adversarial Network for Optical Coherence Tomography Image -- Region-Based Segmentation of Capillary Density in Optical Coherence Tomography Angiography -- An ampli?b￾ed-target loss approach for photoreceptor layer segmentation in pathological OCT scans -- Foveal avascular zone segmentation in clinical routine ?b‚uorescein angiographies using multitask learning -- Guided M-Net for High-resolution Biomedical Image Segmentation with Weak Boundaries -- 3D-CNN for Glaucoma Detection using Optical Coherence Tomography -- Semi-supervised Adversarial Learning for Diabetic Retinopathy Screening -- Shape Decomposition of Foveal Pit Morphology using Scan Geometry Corrected OCT -- U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography -- Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal Images -- Robust Optic Disc Localization by Large Scale Learning -- The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detections -- Fundus Image based Retinal Vessel Segmentation Utilizing A Fast and Accurate Fully Convolutional Network -- Network pruning for OCT image classi?b￾cation -- An improved MPB-CNN segmentation method for edema area and neurosensory retinal detachment in SD-OCT images -- Encoder-Decoder Attention Network for Lesion Segmentation of Diabetic Retinopathy -- Multi-Discriminator Generative Adversarial Networks for improved thin retinal vessel segmentation -- Fovea Localization in Fundus Photographs by Faster R-CNN with Physiological Prior -- Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis via a Deep Convolutional Network -- Automated Stage Analysis of Retinopathy of Prematurity Using Joint Segmentation and Multi-Instance Learning -- Retinopathy Diagnosis using Semi-supervised Multi-channel Generative Adversarial Network. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Ophthalmic Medical Image Analysis : 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings Tipo de documento: documento electrónico Autores: Fu, Huazhu, ; Garvin, Mona K., ; MacGillivray, Tom, ; Xu, Yanwu, ; Zheng, Yalin, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: IX, 218 p. 19 ilustraciones ISBN/ISSN/DL: 978-3-030-63419-3 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 Sistemas de reconocimiento de patrones IngenierÃa Informática Red de computadoras Reconocimiento de patrones automatizado IngenierÃa Informática y Redes Clasificación: Resumen: Este libro constituye las actas arbitradas del 6º Taller Internacional sobre Análisis de Imágenes Médicas Oftálmicas, OMIA 2020, celebrado junto con la 23ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, en Lima, Perú, en octubre de 2020. El taller se realizó de manera virtual debido a la crisis del COVID-19. Los 21 artÃculos presentados en OMIA 2020 fueron cuidadosamente revisados ​​y seleccionados entre 34 presentaciones. Los artÃculos cubren diversos temas en el campo del análisis de imágenes médicas oftálmicas y desafÃos en términos de confiabilidad y validación, número y tipo de condiciones consideradas, análisis multimodal (p. ej., fondo de ojo, tomografÃa de coherencia óptica, oftalmoscopia con láser de escaneo), nuevas tecnologÃas de imágenes. y la transferencia efectiva de tecnologÃas avanzadas de visión por computadora y aprendizaje automático. Nota de contenido: Bio-Inspired Attentive Segmentation of Retinal OCT imaging -- DR detection using Optical Coherence Tomography Angiography (OCTA): a transfer learning approach with robustness analysis -- What is the optimal attribution method for explainable ophthalmic disease classification? -- DeSupGAN: Multi-scale Feature Averaging Generative Adversarial Network for Simultaneous De-blurring and Super-resolution of Retinal Fundus Images -- Encoder-Decoder Networks for Retinal Vessel Segmentation using Large Multi-Scale Patches -- Retinal Image Quality Assessment via Specific Structures Segmentation -- Cascaded Attention Guided Network for Retinal Vessel Segmentation -- Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography -- Automated Detection of Diabetic Retinopathy From Smartphone Fundus Videos -- Optic Disc, Cup and Fovea Detection from Retinal Images using U-Net++ with EfficientNet Encoder -- Multi-level Light U-Net and Atrous Spatial Pyramid Poolingfor Optic Disc Segmentation on Fundus Image -- An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering -- Retinal OCT Denoising with Pseudo-Multimodal Fusion Network -- Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling -- Weakly supervised retinal detachment segmentation using deep feature propagation learning in SD-OCT images -- A framework for the discovery of retinal biomarkers in Optical Coherence Tomography Angiography (OCTA) -- An Automated Aggressive Posterior Retinopathy of Prematurity Diagnosis System by Squeeze and Excitation Hierarchical Bilinear Pooling Network -- Weakly-Supervised Lesion-aware and Consistency Regularization for Retinitis Pigmentosa Detection from Ultra-widefield Images -- A Conditional Generative Adversarial Network-based Method for Eye Fundus Image Quality Enhancement -- Construction of quantitative indexes for cataract surgery evaluation based on deep learning -- Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Ophthalmic Medical Image Analysis : 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings [documento electrónico] / Fu, Huazhu, ; Garvin, Mona K., ; MacGillivray, Tom, ; Xu, Yanwu, ; Zheng, Yalin, . - 1 ed. . - [s.l.] : Springer, 2020 . - IX, 218 p. 19 ilustraciones.
ISBN : 978-3-030-63419-3
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 Sistemas de reconocimiento de patrones IngenierÃa Informática Red de computadoras Reconocimiento de patrones automatizado IngenierÃa Informática y Redes Clasificación: Resumen: Este libro constituye las actas arbitradas del 6º Taller Internacional sobre Análisis de Imágenes Médicas Oftálmicas, OMIA 2020, celebrado junto con la 23ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, en Lima, Perú, en octubre de 2020. El taller se realizó de manera virtual debido a la crisis del COVID-19. Los 21 artÃculos presentados en OMIA 2020 fueron cuidadosamente revisados ​​y seleccionados entre 34 presentaciones. Los artÃculos cubren diversos temas en el campo del análisis de imágenes médicas oftálmicas y desafÃos en términos de confiabilidad y validación, número y tipo de condiciones consideradas, análisis multimodal (p. ej., fondo de ojo, tomografÃa de coherencia óptica, oftalmoscopia con láser de escaneo), nuevas tecnologÃas de imágenes. y la transferencia efectiva de tecnologÃas avanzadas de visión por computadora y aprendizaje automático. Nota de contenido: Bio-Inspired Attentive Segmentation of Retinal OCT imaging -- DR detection using Optical Coherence Tomography Angiography (OCTA): a transfer learning approach with robustness analysis -- What is the optimal attribution method for explainable ophthalmic disease classification? -- DeSupGAN: Multi-scale Feature Averaging Generative Adversarial Network for Simultaneous De-blurring and Super-resolution of Retinal Fundus Images -- Encoder-Decoder Networks for Retinal Vessel Segmentation using Large Multi-Scale Patches -- Retinal Image Quality Assessment via Specific Structures Segmentation -- Cascaded Attention Guided Network for Retinal Vessel Segmentation -- Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography -- Automated Detection of Diabetic Retinopathy From Smartphone Fundus Videos -- Optic Disc, Cup and Fovea Detection from Retinal Images using U-Net++ with EfficientNet Encoder -- Multi-level Light U-Net and Atrous Spatial Pyramid Poolingfor Optic Disc Segmentation on Fundus Image -- An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering -- Retinal OCT Denoising with Pseudo-Multimodal Fusion Network -- Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling -- Weakly supervised retinal detachment segmentation using deep feature propagation learning in SD-OCT images -- A framework for the discovery of retinal biomarkers in Optical Coherence Tomography Angiography (OCTA) -- An Automated Aggressive Posterior Retinopathy of Prematurity Diagnosis System by Squeeze and Excitation Hierarchical Bilinear Pooling Network -- Weakly-Supervised Lesion-aware and Consistency Regularization for Retinitis Pigmentosa Detection from Ultra-widefield Images -- A Conditional Generative Adversarial Network-based Method for Eye Fundus Image Quality Enhancement -- Construction of quantitative indexes for cataract surgery evaluation based on deep learning -- Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Ophthalmic Medical Image Analysis : 8th International Workshop, OMIA 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings Tipo de documento: documento electrónico Autores: Fu, Huazhu, ; Garvin, Mona K., ; MacGillivray, Tom, ; Xu, Yanwu, ; Zheng, Yalin, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: IX, 200 p. 7 ilustraciones ISBN/ISSN/DL: 978-3-030-87000-3 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 Sistemas de reconocimiento de patrones IngenierÃa Informática Red de computadoras Reconocimiento de patrones automatizado IngenierÃa Informática y Redes Clasificación: Resumen: Este libro constituye las actas arbitradas del 8.° Taller internacional sobre análisis de imágenes médicas oftálmicas, OMIA 2021, celebrado junto con la 24.° Conferencia internacional sobre imágenes médicas e intervención asistida por computadora, MICCAI 2021, en Estrasburgo, Francia, en septiembre de 2021.* Los 20 artÃculos presentados en OMIA 2021 fueron cuidadosamente revisados ​​y seleccionados entre 31 presentaciones. Los artÃculos cubren diversos temas en el campo del análisis de imágenes médicas oftálmicas y desafÃos en términos de confiabilidad y validación, número y tipo de condiciones consideradas, análisis multimodal (p. ej., fondo de ojo, tomografÃa de coherencia óptica, oftalmoscopia con láser de barrido), nuevas tecnologÃas de imágenes. y la transferencia efectiva de tecnologÃas avanzadas de visión por computadora y aprendizaje automático. *El taller se realizó de manera virtual. Nota de contenido: Adjacent Scale Fusion and Corneal Position Embedding for Corneal Ulcer Segmentation -- Longitudinal detection of diabetic retinopathy early severity grade changes using deep learning -- Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach using High Quality iOCT 3D Scans -- Diabetic Retinopathy Detection based on Weakly Supervised Object Localization and Knowledge Driven Attribute Mining -- FARGO: A Joint Framework for FAZ and RV Segmentation from OCTA Images -- CDLRS: Collaborative Deep Learning Model with Joint Regression and Segmentation for Automatic Fovea Localization -- U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina -- Radial U-Net: Improving DMEK Graft Detachment Segmentation in Radial AS-OCT Scans -- Guided Adversarial Adaptation Network for Retinal and Choroidal Layer Segmentation -- Juvenile Refractive Power Prediction based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network -- Peripapillary Atrophy Segmentation with Boundary Guidance -- Are cardiovascular risk scores from genome and retinal image complementary? A deep learning investigation in a diabetic cohort -- Dual-branch Attention Network and Atrous Spatial Pyramid Pooling for Diabetic Retinopathy Classification Using Ultra-Widefield Images -- Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images -- Multi-Modality Images Analysis: A Baseline for Glaucoma Grading via Deep Learning -- Impact of data augmentation on retinal OCT image segmentation for diabetic macular edema analysis -- Representation and Reconstruction of Image-Based Structural Patterns of Glaucomatous Defects Using Only Two Latent Variables from a Variational Autoencoder -- Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification -- Attention Guided Slit Lamp Image Quality Assessment -- Robust Retinal Vessel Segmentation from a Data Augmentation Perspective. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Ophthalmic Medical Image Analysis : 8th International Workshop, OMIA 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings [documento electrónico] / Fu, Huazhu, ; Garvin, Mona K., ; MacGillivray, Tom, ; Xu, Yanwu, ; Zheng, Yalin, . - 1 ed. . - [s.l.] : Springer, 2021 . - IX, 200 p. 7 ilustraciones.
ISBN : 978-3-030-87000-3
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 Sistemas de reconocimiento de patrones IngenierÃa Informática Red de computadoras Reconocimiento de patrones automatizado IngenierÃa Informática y Redes Clasificación: Resumen: Este libro constituye las actas arbitradas del 8.° Taller internacional sobre análisis de imágenes médicas oftálmicas, OMIA 2021, celebrado junto con la 24.° Conferencia internacional sobre imágenes médicas e intervención asistida por computadora, MICCAI 2021, en Estrasburgo, Francia, en septiembre de 2021.* Los 20 artÃculos presentados en OMIA 2021 fueron cuidadosamente revisados ​​y seleccionados entre 31 presentaciones. Los artÃculos cubren diversos temas en el campo del análisis de imágenes médicas oftálmicas y desafÃos en términos de confiabilidad y validación, número y tipo de condiciones consideradas, análisis multimodal (p. ej., fondo de ojo, tomografÃa de coherencia óptica, oftalmoscopia con láser de barrido), nuevas tecnologÃas de imágenes. y la transferencia efectiva de tecnologÃas avanzadas de visión por computadora y aprendizaje automático. *El taller se realizó de manera virtual. Nota de contenido: Adjacent Scale Fusion and Corneal Position Embedding for Corneal Ulcer Segmentation -- Longitudinal detection of diabetic retinopathy early severity grade changes using deep learning -- Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach using High Quality iOCT 3D Scans -- Diabetic Retinopathy Detection based on Weakly Supervised Object Localization and Knowledge Driven Attribute Mining -- FARGO: A Joint Framework for FAZ and RV Segmentation from OCTA Images -- CDLRS: Collaborative Deep Learning Model with Joint Regression and Segmentation for Automatic Fovea Localization -- U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina -- Radial U-Net: Improving DMEK Graft Detachment Segmentation in Radial AS-OCT Scans -- Guided Adversarial Adaptation Network for Retinal and Choroidal Layer Segmentation -- Juvenile Refractive Power Prediction based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network -- Peripapillary Atrophy Segmentation with Boundary Guidance -- Are cardiovascular risk scores from genome and retinal image complementary? A deep learning investigation in a diabetic cohort -- Dual-branch Attention Network and Atrous Spatial Pyramid Pooling for Diabetic Retinopathy Classification Using Ultra-Widefield Images -- Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images -- Multi-Modality Images Analysis: A Baseline for Glaucoma Grading via Deep Learning -- Impact of data augmentation on retinal OCT image segmentation for diabetic macular edema analysis -- Representation and Reconstruction of Image-Based Structural Patterns of Glaucomatous Defects Using Only Two Latent Variables from a Variational Autoencoder -- Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification -- Attention Guided Slit Lamp Image Quality Assessment -- Robust Retinal Vessel Segmentation from a Data Augmentation Perspective. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
