AIMC Topic: Fundus Oculi
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DISCERN: Generative Framework for Vessel Segmentation using Convolutional Neural Network and Visual Codebook.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
This paper presents a novel two-stage vessel segmentation framework applied to retinal fundus images. In the first stage a convolutional neural network (CNN) is used to correlate an image patch with a corresponding groundtruth reduced using Totally R...
A New and Improved Method for Automated Screening of Age-Related Macular Degeneration Using Ensemble Deep Neural Networks.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
In this paper, we provide a new framework on deep learning based automated screening method for finding individuals at risk of developing Age-related Macular Degeneration (AMD). We studied the appropriateness of using the transfer learning to screen ...
Deep Learning for Predicting Refractive Error From Retinal Fundus Images.
Investigative ophthalmology & visual science
Jun 1, 2018
PURPOSE: We evaluate how deep learning can be applied to extract novel information such as refractive error from retinal fundus imaging.
Automated System for Referral of Cotton-Wool Spots.
Current diabetes reviews
Jan 1, 2018
BACKGROUND: Cotton-wool spots also referred as soft exudates are the early signs of complications in the eye fundus of the patients suffering from diabetic retinopathy. Early detection of exudates helps in the diagnosis of the disease and provides be...
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.
JAMA ophthalmology
Nov 1, 2017
IMPORTANCE: Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying indi...
Image quality classification for DR screening using deep learning.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2017
The quality of input images significantly affects the outcome of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that only consider simple low-level features such as hand-crafted geometric and structural features, i...
Automatic Identification of Glaucoma Using Deep Learning Methods.
Studies in health technology and informatics
Jan 1, 2017
This paper proposes an automatic classification method to detect glaucoma in fundus images. The method is based on training a neural network using public image databases. The network used in this paper is the GoogLeNet, adapted for this proposal. The...
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
JAMA
Dec 13, 2016
IMPORTANCE: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of the...
Classification of large-scale fundus image data sets: a cloud-computing framework.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Aug 1, 2016
Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce comput...
Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.
Journal of digital imaging
Dec 1, 2015
Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making syst...
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