Method
In this work, we aim to solve the problem that segmenting new target retinal fundus images with dissimilar source training set. We present a two-step supervised learning approach . The pipline of our method is shown in Figure. 1. The first step focuses on the construction of a synthesized dataset based on the target query images. The second step proceeds to learn a supervised segmentation method based on the synthesized dataset. The constructed dataset is capable to bridge the gap between the existing source dataset and the new target query dataset. This is the crucial part of our approach. The technique used to build the synthesized data is recurrent generative adversarial network (R-sGAN) whose details are shown in Figure. 2. With the help of R-sGAN, the realistic-looking training images can be generated containing the content of source dataset with the same textural style of the target query images. The model structure of our R-sGAN is displayed in Figure. 1(a) with detailed GRU structure depicted in Figure. 1(b). The involved loss fuctions are summarized in Figure. 1(c). Figure. 1(d) presents the network used for feature extraction.