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Ophthalmic Medical Image Analysis6th...

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Ophthalmic Medical Image Analysis6th International Workshop, OMIA 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Ophthalmic Medical Image Analysisedited by Huazhu Fu ... [et al.].
Reminder of title:	6th International Workshop, OMIA 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17 : proceedings /
remainder title:	OMIA 2019
other author:	Fu, Huazhu.
corporate name:
Published:	Cham :Springer International Publishing :2019.
Description:	xi, 192 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	PathologyCongresses.Data processing
Online resource:	https://doi.org/10.1007/978-3-030-32956-3
ISBN:	9783030329563$q(electronic bk.)

Ophthalmic Medical Image Analysis6th International Workshop, OMIA 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17 : proceedings /
Ophthalmic Medical Image Analysis6th International Workshop, OMIA 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17 : proceedings /[electronic resource] :OMIA 2019edited by Huazhu Fu ... [et al.]. - Cham :Springer International Publishing :2019. - xi, 192 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,118550302-9743 ;. - Lecture notes in computer science ;4891..

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 ampliﬁed-target loss approach for photoreceptor layer segmentation in pathological OCT scans -- Foveal avascular zone segmentation in clinical routine ﬂuorescein 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 classiﬁcation -- 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.

This book constitutes the refereed proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 22 full papers (out of 36 submissions) presented at OMIA 2019 were carefully reviewed and selected. The papers cover various topics in the field of ophthalmic image analysis.

ISBN: 9783030329563$q(electronic bk.)

Standard No.: 10.1007/978-3-030-32956-3doiSubjects--Topical Terms:

823085
Pathology
--Data processing--Congresses.

LC Class. No.: RB38 / .O55 2019

Dewey Class. No.: 616.07

Ophthalmic Medical Image Analysis6th International Workshop, OMIA 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17 : proceedings /
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505 0 $a 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 ampliﬁed-target loss approach for photoreceptor layer segmentation in pathological OCT scans -- Foveal avascular zone segmentation in clinical routine ﬂuorescein 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 classiﬁcation -- 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.
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