A Domain Adaptation Multi-instance Learning for Diabetic Retinopathy Grading on Retinal Images

Diabetic retinopathy (DR) is one of the most concerning, common and serious diseases in the ophthalmology community. Early detection and treatment of DR can significantly reduce the risk of vision loss in patients. Traditional DR automatic classification algorithms rely on the precise detection of microaneurysms (MA) and hemorrhage (H) lesions. Such lesion annotation is an expensive and time-consuming process, hence it is expected to develop automatic grading methods with only image-level annotations. The lack of the position of MA and H hinders the traditional supervised algorithms for the accurate identification. In our work, we formulate the weakly supervised DR grading as a multi-instance learning problem, and propose a domain adaptation multi-instance learning with attention mechanism for DR grading. Specifically, labeled instances are generated by cross-domain to filter irrelevant instances in the target domain. To model the relationship between the suspicious instances and bag label, a multi-instance learning with attention mechanism is developed to acquire the location information of highly suspected lesions and predict the grade of DR. We evaluate our proposed algorithm on the Messidor dataset, and the experimental results demonstrate that it achieves an average accuracy of 0.764 and an AUC value of 0.749 respectively, outperforming state-of-the-art approaches.