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Diagnostic Techniques, Ophthalmological

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Deep learning in ophthalmology: The technical and clinical considerations.

Progress in retinal and eye research
The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for ...

A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images.

IEEE transactions on medical imaging
Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a...

Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment.

IEEE transactions on medical imaging
Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labeled database and a large unlabeled database, the goal is to train a powerful classifie...

Evaluation of deep convolutional neural networks for glaucoma detection.

Japanese journal of ophthalmology
PURPOSE: To investigate the performance of deep convolutional neural networks (DCNNs) for glaucoma discrimination using color fundus images STUDY DESIGN: A retrospective study PATIENTS AND METHODS: To investigate the discriminative ability of 3 DCNNs...

Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation.

IEEE transactions on medical imaging
Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and optic cup (OC) from fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks demonstrate promising...

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.

IEEE journal of biomedical and health informatics
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...

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

Medical image analysis
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...

Weakly Supervised Lesion Detection From Fundus Images.

IEEE transactions on medical imaging
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...