An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images.

ABSTRACT: Diabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for monitoring their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to retinopathy severity estimates for patients in remote regions or even for complementing the human expert's diagnosis. Here we propose a machine learning system for the detection of referable diabetic retinopathy in fundus images, which is based on the paradigm of multiple-instance learning. Our

SUBMITTER: Papadopoulos A 

PROVIDER: S-EPMC8275626 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature