Feng Li

TL;DR: Deep transfer learning method based on the VGG-16 network shows significant effectiveness on classification of retinal OCT images with a relatively small dataset, which can provide assistant support for medical decision-making.

TL;DR: A 4-class classification problem to automatically detect choroidal neovascularization, diabetic macular edema, DRUSEN, and NORMAL in optical coherence tomography (OCT) images and the effect of the integration of retinal OCT images and medical history data from patients on model performance is explored.

TL;DR: This approach could automatically detect DR with excellent sensitivity, accuracy, and specificity and could aid in making a referral recommendation for further evaluation and treatment with high reliability.

TL;DR: This approach could discriminate GON with high accuracy, sensitivity, specificity, and AUC using color fundus photographs, and may provide a second opinion on the diagnosis of glaucoma to the specialist quickly, efficiently and at low cost, and assist doctors and the public in large-scale screening for glAUcoma.

TL;DR: Wang et al. as discussed by the authors presented and validated a deep ensemble algorithm to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) using retinal fundus images.