On Detection of Diabetic Retinopathy via Multiple Instance Learning

Diabetic Retinopathy (DR) is a complication of diabetes, caused by a damage to the blood vessels in the light-sensitive tissue of the retina. Since it affects the eyes, it can determine visual impairment or even blindness. Considering the number of diabetic patients worldwide, it is clear that effective screening of potential DR patients is of utmost importance. While direct and indirect ophthalmoscopy are the main methods for evaluating DR, artificial intelligence is on the rise in vision care. DR is detectable by analyzing data from patients’ fundus photographs, and is therefore a disease that artificial intelligence tools can effectively support. In this paper, we present some preliminary numerical results obtained in discriminating between eye fundi of healthy individuals and of people with severe diabetic retinopathy, by using a Multiple Instance Learning approach.