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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 …