  Active Learning methods create an optimized labeled training set from
unlabeled data. We introduce a novel Online Active Deep Learning method for
Medical Image Analysis. We extend our MedAL active learning framework to
present new results in this paper. Our novel sampling method queries the
unlabeled examples that maximize the average distance to all training set
examples. Our online method enhances performance of its underlying baseline
deep network. These novelties contribute significant performance improvements,
including improving the model's underlying deep network accuracy by 6.30%,
using only 25% of the labeled dataset to achieve baseline accuracy, reducing
backpropagated images during training by as much as 67%, and demonstrating
robustness to class imbalance in binary and multi-class tasks.
