Efficient multi-kernel multi-instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis.

OBJECTIVE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes. Early detection and treatment of DR are key public health interventions that can significantly reduce the risk of vision loss. How to effectively screen and diagnose the retinal fundus image in order to identify retinopathy in time is a major challenge. In the traditional DR screening system, the accuracy of micro-aneurysm (MA) and hemorrhagic (H) lesion detection determines the final screening performance. The detection method produced a large number of false positive samples for guaranteeing high sensitivity, and the classification model was not effective in removing false positives since the suspicious lesions lack label information.

METHODS: In order to solve the problem of supervised learning in the diagnosis of DR, we formulate weakly supervised multi-class DR grading as a multi-class multi-instance problem where each image (bag) is labeled as healthy or abnormal and consists of unlabeled candidate lesion regions (instances). Specifically, we proposed a multi-kernel multi-instance learning method based on graph kernel. Moreover, we develop an under-sampling from instance level and over-sampling from bag level to improve the performance of the multi-instance learning in the diagnosis of DR.

RESULTS: Through empirical evaluation and comparison with different baselinemethods and the state-of-the-art methods on data from Messidor, we illustrate that the proposed method reports favorable results, with an overall classification accuracy of 0.916 and an AUC of 0.957.

CONCLUSIONS: The experiments results demonstrate that the proposed multi-kernel multi-instance learning framework with bi-level re-sampling can solve the problem in the imbalanced and weakly supervised data for grading diabetic retinopathy, and it improves the diagnosis performance over several state-of-the-art competing methods.