Microaneurysm Detection With Multiscale Attention and Trident RPN

Abstract

Diabetic retinopathy (DR) is the most serious and common complication of diabetes. Microaneurysm (MA) detection is of great importance for DR screening by providing the earliest indicator of presence of DR. Extremely small size of MAs, low color contrast in fundus images, and the interference from blood vessels and other lesions with similar characteristics make MA detection still challenging. In this paper, a novel two-stage MA detector with multiscale attention and trident Region proposal network (RPN) is proposed. A scale selection pyramid network based on the attention mechanism is established to improve detection performance on the small objects by reducing the gradient inconsistency between low and high level features. Meanwhile, a trident RPN with three-branch parallel feature enhance head is designed to promote more distinguishing learning, further reducing the misrecognition. The proposed method is validated on IDRiD, e-ophtha, and ROC datasets with the average scores of 0.516, 0.646, and 0.245, respectively, achieving the best or nearly optimal performance compared to the state-of-the-arts. Besides, the proposed MA detector illustrates a more balanced performance on the three datasets, showing strong generalization.