EP-Net: Automatic Artery/Vein Classification With Evidential Probability Map

Rongchang Zhao, Bo Xu, Xiaoliang Jia, Jin Liu

Abstract: Abnormal retinal vascular morphology is commonly associated with cardiac, cerebrovascular, and systemic diseases. Hence, automated artery/vein(A/V) classification is crucial for the diagnosis of ophthalmic and systemic diseases. However, existing methods still face limitations in A/V classification and are prone to errors especially in microvessels and in noisy backgrounds. To alleviate these problems, this paper proposes an Evidence Probability Network (EP-Net) to achieve accurate A/V classification. Concretely, the multi-scale feature module in the EP-Net learns various vessel features, and the evidence probability module measures uncertainty and evidence for each pixel to overcome misclassification because of over-/under-confidence. Experiments on two public fundus image datasets demonstrate the superiority of the proposed EP-Net over state-of-the-art A/V classification methods.