Attention-guided Deep Multi-instance Learning for Staging Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is one of the commonest causes of acquired blindness in children. The stage of ROP is an important step to evaluate the ROP severity for disease control and management. However, there are still various challenges for ROP stage since the pattern of ROP is relatively obscure compared to the entire fundus image. Also, the dataset is small and the image quality is quite poor. To address these issues, we develop a multi-instance learning (MIL) network, which can extract the features of the images and these features can be enhanced by a fully convolutional network (FCN). The spatial score map (SSM) produced by the FCN is cropped into small patches and fed into the proposed MIL for further feature learning. An attention mechanism is leveraged to guide the MIL pooling, which can focus on the ROP features of different stages and improve the staging results. The proposed network is evaluated on an inhouse ROP dataset and experimental results demonstrate that our proposed method is promising for the stage of ROP.