In diabetic eye screening programme, a special pathway is designed for those who have received laser photocoagulation treatment. The treatment leaves behind circular or irregular scars in the retina. Laser scar detection in fundus images is thus important for automated DR screening. Despite its importance, the problem is understudied in terms of both datasets and methods. This paper makes the first attempt to detect laser-scar images by deep learning. To that end, we contribute to the community Fundus10K, a large-scale expert-labeled dataset for training and evaluating laser scar detectors. We study in this new context major design choices of state-of-the-art Convolutional Neural Networks including Inception-v3, ResNet and DenseNet. For more effective training we exploit transfer learning that passes on trained weights of ImageNet models to their laser-scar counterparts. Experiments on the new dataset. shows that our best model detects laser-scar images with sensitivity of 0.962, specificity of 0.999, precision of 0.974 and AP of 0.988 and AUG of 0.999. The same model is tested on the public: LMD-BAPT test set, obtaining sensitivity of 0.765, specificity of 1, precision of 1, AP of 0.975 and AUG of 0.991, outperforming the state-of-the-art with a large margin. Data is available at https://github.com/li-xirong/ fundus10k/.
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