Automatic zoning for retinopathy of prematurity with semi-supervised feature calibration adversarial learning.

Retinopathy of prematurity (ROP) is an eye disease, which affects prematurely born infants with low birth weight and is one of the main causes of children's blindness globally. In recent years, there are many studies on automatic ROP diagnosis, mainly focusing on ROP screening such as "Yes/No ROP" or "Mild/Severe ROP" and presence/absence detection of "plus disease". Due to the lack of corresponding high-quality annotations, there are few studies on ROP zoning, which is one of the important indicators to evaluate the severity of ROP. Moreover, how to effectively utilize the unlabeled data to train model is also worth studying. Therefore, we propose a novel semi-supervised feature calibration adversarial learning network (SSFC-ALN) for 3-level ROP zoning, which consists of two subnetworks: a generative network and a compound network. The generative network is a U-shape network for producing the reconstructed images and its output is taken as one of the inputs of the compound network. The compound network is obtained by extending a common classification network with a discriminator, introducing adversarial mechanism into the whole training process. Because the definition of ROP tells us where and what to focus on in the fundus images, which is similar to the attention mechanism. Therefore, to further improve classification performance, a new attention mechanism based feature calibration module (FCM) is designed and embedded in the compound network. The proposed method was evaluated on 1013 fundus images of 108 patients with 3-fold cross validation strategy. Compared with other state-of-the-art classification methods, the proposed method achieves high classification performance.