Ophthalmic Medical Image Analysis : 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, Proceedings / edited by Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng.

Format:

Book

Contributor:

Fu, Huazhu, Editor.

Garvin, Mona K., Editor.

MacGillivray, Tom, Editor.

Xu, Yanwu, Editor.

Zheng, Yalin, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11855

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11855

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Computer science-Mathematics.

Computer engineering.

Computer networks.

Computer Vision.

Artificial Intelligence.

Mathematics of Computing.

Computer Engineering and Networks.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Mathematics of Computing.

Computer Engineering and Networks.

Physical Description:

1 online resource (XI, 192 pages) : 80 illustrations, 78 illustrations in color.

Edition:

1st ed. 2019.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-32956-3

9783030329563

Access Restriction:

Restricted for use by site license.