Diabetic Retinopathy (DR) is the major cause of blindness, which seriously threatens the
world's vision health. Limited medical resources make early diagnosis and a large-scale …

This paper assesses the efficacy of self-supervised learning in the DeepDR Diabetic
Retinopathy Image Dataset (DeepDRiD). Recently, self-supervised learning has achieved …

Diabetic retinopathy (DR) gives rise to blindness in young adults around the world. By early
detection, patients with DR can be properly treated in time, and the deterioration of DR can …

Longitudinal imaging is able to capture both static anatomical structures and dynamic
changes in disease progression towards earlier and better patient-specific pathology …

Progress in medical imaging models using supervised learning has reached closer to
clinical-level performance of doctors. However, labeling huge amounts of medical data …

Large-scale datasets, which have sufficient and identical quantities of data in each class, are
the main factor in the success of deep-learning-based classification models for vision tasks …

Annotated training data insufficiency remains to be one of the challenges of applying deep
learning in medical data classification problems. Transfer learning from an already trained …

Diabetic Retinopathy (DR) is a high blood sugar level that causes damage to blood vessels
and is one of the common causes of blindness in the developed world. Convolutional Neural …

Diabetic Retinopathy (DR) is a major complication in human eyes among the diabetic
patients. Early detection of the DR can save many patients from permanent blindness …

Diabetic retinopathy (DR) is the leading cause of blindness in diabetics. The low contrast
and microscopic nature of the lesions lead to a high false positive rate for automated DR …