A framework for identifying diabetic retinopathy based on anti-noise detection and attention-based fusion

Abstract

Automatic diagnosis of diabetic retinopathy (DR) using retinal fundus images is a challenging problem because images of low grade DR may contain only a few tiny lesions which are difficult to perceive even to human experts. Using annotations in the form of lesion bounding boxes may help solve the problem by deep learning models, but fully annotated samples of this type are usually expensive to obtain. Missing annotated samples (i.e., true lesions but not included in annotations) are noise and can affect learning models negatively. Besides, how to utilize lesion information for identifying DR should be considered carefully because different types of lesions may be used to distinguish different DR grades. In this paper, we propose a new framework for unifying lesion detection and DR identification. Our lesion detection model first determines the missing annotated samples to reduce their impact on the model, and extracts lesion information. Our attention-based network then fuses original images and lesion information to identify DR. Experimental results show that our detection model can considerably reduce the impact of missing annotation and our attention-based network can learn weights between the original images and lesion information for distinguishing different DR grades. Our approach outperforms state-of-the-art methods on two grand challenge retina datasets, EyePACS and Messidor.