Hard exudate segmentation in retinal image with attention mechanism

Diabetic retinopathy (DR) is the main reason that causes preventable blindness. Hard exudate is one of the earliest signs of diabetic retinopathy. Precise detection of hard exudate is helpful for the early diagnosis of diabetic retinopathy. Fully convolutional network (FCN) shows great performance on hard exudate segmentation task. However, there are limitations for fully convolutional network to build long-range dependencies in different regions of the image. Convolution operator extract features in local area, segmentation results based on local features are likely to be wrong in some cases. Another channel attention method was proposed, and two different attention modules are used in the segmentation model. In this way, long-range dependencies across different image regions are built efficiently in different stages of feature extraction. In addition, a new loss function is designed to deal with the data imbalance problem in hard exudate segmentation task. The proposed method was evaluated by two public datasets, and the comparative experiments show the effectiveness of the proposed method.