AIMC Topic: Fundus Oculi
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Utility of a public-available artificial intelligence in diagnosis of polypoidal choroidal vasculopathy.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Nov 4, 2019
PURPOSE: To investigate the feasibility of training an artificial intelligence (AI) on a public-available AI platform to diagnose polypoidal choroidal vasculopathy (PCV) using indocyanine green angiography (ICGA).
Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning.
Medical image analysis
Oct 31, 2019
Multi-indices quantification of optic nerve head (ONH), measuring ONH appearance with multiple types of indices simultaneously from fundus images, is the most clinically significant tasks for accurate ONH assessment and ophthalmic disease diagnosis. ...
DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images.
Medical hypotheses
Oct 10, 2019
Recent studies have shown that convolutional neural networks (CNNs) can be more accurate, efficient and even deeper on their training if they include direct connections from the layers close to the input to those close to the output in order to trans...
Joint optic disc and cup segmentation using semi-supervised conditional GANs.
Computers in biology and medicine
Oct 10, 2019
Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important measurements used for glaucoma screening and diagnosis, requires accurate segmentation of optic disc...
REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs.
Medical image analysis
Oct 8, 2019
Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glauco...
Retinal vascular junction detection and classification via deep neural networks.
Computer methods and programs in biomedicine
Sep 27, 2019
BACKGROUND AND OBJECTIVES: The retinal fundus contains intricate vascular trees, some of which are mutually intersected and overlapped. The intersection and overlapping of retinal vessels represent vascular junctions (i.e. bifurcation and crossover) ...
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study.
The Lancet. Digital health
Sep 5, 2019
BACKGROUND: Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifie...
Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.
Artificial intelligence in medicine
Aug 7, 2019
Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosi...
Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.
International journal of medical informatics
Aug 5, 2019
BACKGROUND: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set u...
Automatic cataract grading methods based on deep learning.
Computer methods and programs in biomedicine
Aug 5, 2019
BACKGROUND AND OBJECTIVE: The shortage of ophthalmologists in rural areas in China causes a lot of cataract patients not getting timely diagnosis and effective treatment. We develop an algorithm and platform to automatically diagnose and grade catara...
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