Multiple Instance Learning for Diabetic Retinopathy Detection

Diabetic Retinopathy (DR) is a complication of the diabetes, caused by a damage to the blood vessels in the light-sensitive tissue of the retina: consequently it affects the eyes, determining blindness and visual impairment. On the basis of the last report of the International Diabetes Federation, 537 million adults (20-79 years) were living with diabetes (1 in 10) in 2021, being the cause of 6.7 million deaths (1 every 5 seconds). Taking into account that the number of people affected by DR is predicted to increase to 643 million by 2030 and 700 million by 2045, 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 numerical results obtained in the classification between eye fundi of healthy individuals and of people with severe DR, using a Multiple Instance Learning approach.