Experiments on three medical image datasets show that our novel online active learning model requires significantly less labelings, is more accurate, and is more robust to class imbalances than existing methods.
Annotated training data insufficiency remains to be one of the challenges of applying deep learning in medical data classification problems.
Given estimates of p(y|x) from a predictive model, Saerens et al. (2002) proposed an efficient EM algorithm to correct for label shift that does not require model retraining.
We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection.