Ophthalmic Medical Image Analysis (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
192
Utgivningsdatum
2019-10-18
Upplaga
1st ed. 2019
Förlag
Springer Nature Switzerland AG
Medarbetare
Garvin, Mona K. / MacGillivray, Tom
Illustrationer
78 Illustrations, color; 2 Illustrations, black and white; XI, 192 p. 80 illus., 78 illus. in color.
Dimensioner
234 x 156 x 11 mm
Vikt
295 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030329556
Ophthalmic Medical Image Analysis (häftad)

Ophthalmic Medical Image Analysis

6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, Proceedings

Häftad Engelska, 2019-10-18
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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.
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Innehållsförteckning

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.