Abstract
Diabetic retinopathy (DR) is a common fundus disease that leads to irreversible blindness, which plagues the working-age population. Automatic medical imaging diagnosis provides a non-invasive method to assist ophthalmologists in timely screening of suspected DR cases, which prevents its further deterioration. However, the state-of-the-art deep-learning-based methods generally have a large amount of model parameters, which makes large-scale clinical deployment a time-consuming task. Moreover, the severity of DR is associated with lesions, and it is difficult for the model to focus on these regions. In this paper, we propose a novel deep-learning technique for grading DR with only image-level supervision. Specifically, we first customize the model with the help of self-knowledge distillation to achieve a trade-off between model performance and time complexity. Secondly, CAM-Attention is used to allow the network to focus on discriminative zone, e.g., microaneurysms, soft/hard exudates, etc.. Considering that directly attaching a classifier after the Side branch will disrupt the hierarchical nature of convolutional neural networks, a Mimicking Module is employed that allows the Side branch to actively mimic the main branch structure. Extensive experiments are conducted on two benchmark datasets, with an AUC of 0.965 and an accuracy of 92.9% for the Messidor dataset and 67.96% accuracy achieved for the challenging IDRID dataset, which demonstrates the superior performance of our proposed method.
Highlights
Diabetic retinopathy (DR) is the predominant manifestation of diabetic microangiopathy, which is one of the complications of diabetes
The quantitative results can be summarized as follows: (1) our method improves the AUC metric by nearly 10% compared to the method [30] using laborious manual feature extraction; (2) compared with methods such as Zoom-in-net [31] that use additional data to improve performance, we still achieve outstanding results with only Messidor’s annotations; (3) in contrast to CANet [12], which uses bulky ResNet50 combined with multitask learning, our method uses lightweight ResNet18 while increasing the AUC, accuracy and precision by 0.3%, 0.3% and 0.4%
The self-knowledge distillation (SKD)-refined student (Side branch 1) in the second line has the same classification accuracy (67.96%) as the teacher, while significantly cutting down the number of parameters, which further confirms the efficiency of our proposed method
Summary
Diabetic retinopathy (DR) is the predominant manifestation of diabetic microangiopathy, which is one of the complications of diabetes. It is reported that approximately one third of people with diabetes in the United States, Europe and Asia have some degree of DR [1]. It the major leading cause of blindness and vision defects among working-age adults worldwide [2]. The scarcity of ophthalmologists hinders patients from receiving timely diagnosis and treatment, especially in underdeveloped areas, which eventually leads to irreversible vision loss. With this in mind, an automated computer-aided diagnostic (CAD) system is needed to assist ophthalmologists in the early screening of potential DR, alleviating their labor-intensive workload
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