AIMC Topic: Retina
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Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.
IEEE journal of biomedical and health informatics
Feb 14, 2019
Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central regi...
Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification.
IEEE transactions on medical imaging
Feb 8, 2019
Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases accordi...
Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular Telangiectasia Type 2.
JAMA network open
Feb 1, 2019
IMPORTANCE: As currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians.
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.
Medical image analysis
Jan 31, 2019
Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised le...
Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages.
IEEE/ACM transactions on computational biology and bioinformatics
Jan 14, 2019
Segmenting bioimage based filaments is a critical step in a wide range of applications, including neuron reconstruction and blood vessel tracing. To achieve an acceptable segmentation performance, most of the existing methods need to annotate amounts...
Fully automated detection of retinal disorders by image-based deep learning.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Jan 4, 2019
PURPOSE: With the aging population and the global diabetes epidemic, the prevalence of age-related macular degeneration (AMD) and diabetic macular edema (DME) diseases which are the leading causes of blindness is further increasing. Intravitreal inje...
Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal SD-OCT scans.
Computers in biology and medicine
Dec 31, 2018
Maculopathy is a group of retinal disorders that affect macula and cause severe visual impairment if not treated in time. Many computer-aided diagnostic methods have been proposed over the past that automatically detect macular diseases. However, to ...
Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model.
Computers in biology and medicine
Dec 28, 2018
Automatic and reliable segmentation for geographic atrophy in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. To develop an effective segmentation method, a two-stage deep learning framework based on an auto-encode...
Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage.
Bioscience reports
Dec 7, 2018
There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In ...
Weakly Supervised Lesion Detection From Fundus Images.
IEEE transactions on medical imaging
Dec 6, 2018
Early diagnosis and continuous monitoring of patients suffering from eye diseases have been major concerns in the computer-aided detection techniques. Detecting one or several specific types of retinal lesions has made a significant breakthrough in c...
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