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

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.