  Diabetic Retinopathy (DR) is a leading cause of vision loss globally. Yet
despite its prevalence, the majority of affected people lack access to the
specialized ophthalmologists and equipment required for assessing 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 DR severity estimates for patients in remote
regions or even for complementing the human expert's diagnosis. In this paper,
we propose a machine learning system for the detection of referable DR in
fundus images that is based on the paradigm of multiple-instance learning. By
extracting local information from image patches and combining it efficiently
through an attention mechanism, our system is able to achieve high
classification accuracy. Moreover, it can highlight potential image regions
where DR manifests through its characteristic lesions. We evaluate our approach
on publicly available retinal image datasets, in which it exhibits near
state-of-the-art performance, while also producing interpretable visualizations
of its predictions.
