MLGL: Model-free Lesion Generation and Learning for Diabetic Retinopathy Diagnosis.

The approaches based on deep learning have achieved remarkable success in diabetic retinopathy detection. Due to the accountability in medical diagnosis, the interpretability of computer-aided diagnosis has recently been investigated. However, few existing approaches make full use of the explainable evidence to improve the diagnosis accuracy. In this paper, we propose a Model-free Lesion Generation and Learning (MLGL) framework to study the interpretability of diabetic retinopathy detection. We first generate visual explanations for diabetic retinopathy diagnosis using the proposed Gated Multi-layer Saliency Map (GMSM) module, which locates the accurate region of lesions by combining multi-layer heatmaps. Then we use the GMSM to extract the lesion patches and conduct the adaptive lesion transfer, iteratively generating new retinal fundus images with lesions. Especially, in this process, no additional generative models are trained. Finally, we merge the generated and original retinal fundus images for the model's training to learn robust lesion features. Overall, our method provides accurate explainable evidence and further addresses the data imbalance problem in diabetic retinopathy detection. The experimental results on four public datasets demonstrate the efficiency of our approach.