Generative adversarial network in medical imaging: A review.

Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.

Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion

A survey on medical image analysis in diabetic retinopathy

Diabetic retinopathy is one of the leading disabling chronic diseases, and one of the leading causes of preventable blindness in the world Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into screening programs and especially into automated screening programs For automated screening programs to work robustly a representative fundus image database is required In this paper we give an overview of currently available databases and present a new diabetic retinopathy database Our database is to our knowledge the first and only database which has diabetic retinopathy pathologies and major fundus structures annotated for every image from the database which makes it perfect for design and evaluation of currently available and new image processing algorithms for early detection of diabetic retinopathy using color fundus images

Diabetic retinopathy image database(DRiDB): A new database for diabetic retinopathy screening programs research

ABSTRACT Bilateral acute iris transillumination (BAIT) is a rare clinical entity, presumed to be associated with preceding upper respiratory tract infection and/or use of certain antibiotics, marked by bilateral acute loss of iris pigment epithelium with pigment dispersion in the anterior chamber and trabecular meshwork, which can cause elevated intraocular pressure and glaucoma, and with iris transillumination and sphincter paralysis which lead to photophobia and blurry vision. We report the first two cases of BAIT in our center which both had a history of preceding COVID-19 (coronavirus disease 2019) and moxifloxacin use. With more awareness, ophthalmologists might diagnose more cases, and thus gain more information regarding the link between COVID-19 and BAIT, which might be underdiagnosed since it is rare or easily misdiagnosed as some more common diseases with similar features.

Raseljka Tadic

Papers

Diabetic retinopathy image database(DRiDB): A new database for diabetic retinopathy screening programs research

Bilateral Acute Iris Transillumination (BAIT): A Rare Syndrome Possibly Associated with COVID-19 and Moxifloxacin Use. A Report of 2 Cases

The analysis of the glaucoma screening event results obtained during the World Glaucoma Week in Zagreb, Croatia