Título :	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  Ingeniería Informática  Red de computadoras  Matemáticas de la Computación  Ingeniería Informática y Redes
Índice Dewey:	006.37 Visión artificial
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 artículos completos (de 36 presentaciones) presentados en OMIA 2019. Los artículos 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.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i

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  Ingeniería Informática  Red de computadoras  Matemáticas de la Computación  Ingeniería Informática y Redes
Índice Dewey:	006.37 Visión artificial
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 artículos completos (de 36 presentaciones) presentados en OMIA 2019. Los artículos 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.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i