Automatic detection of pathological myopia and high myopia on fundus images

Automatic detection of myopia plays a significant role in clinical practice. Few studies have been done on the detection of pathological myopia, and no attention has been paid to the distinguishment between it and high myopia. Additionally, they are hard to differentiate because of the high similarity between them. In this paper, we design a network with two branches for different classification tasks, where the first one is to distinguish the normal and abnormal while the other is to classify pathological myopia and high myopia. We manage to improve the classification accuracy by combining Binary Cross-Entropy loss and Triplet loss. Extensive experiments are conducted for comparison between our method and other universal classification models using a private retinal fundus dataset. The results demonstrate that our method achieves the best performance with 81.82%, 83.61% and 83.52% on the accuracy, precision and sensitivity, respectively.