  Retinal vascular diseases affect the well-being of human body and sometimes
provide vital signs of otherwise undetected bodily damage. Recently, deep
learning techniques have been successfully applied for detection of diabetic
retinopathy (DR). The main obstacle of applying deep learning techniques to
detect most other retinal vascular diseases is the limited amount of data
available. In this paper, we propose a transfer learning technique that aims to
utilize the feature similarities for detecting retinal vascular diseases. We
choose the well-studied DR detection as a source task and identify the early
detection of retinopathy of prematurity (ROP) as the target task. Our
experimental results demonstrate that our DR-pretrained approach dominates in
all metrics the conventional ImageNet-pretrained transfer learning approach,
currently adopted in medical image analysis. Moreover, our approach is more
robust with respect to the stochasticity in the training process and with
respect to reduced training samples. This study suggests the potential of our
proposed transfer learning approach for a broad range of retinal vascular
diseases or pathologies, where data is limited.
