  We propose a highly data-efficient active learning framework for image
classification. Our novel framework combines: (1) unsupervised representation
learning of a Convolutional Neural Network and (2) the Gaussian Process (GP)
method, in sequence to achieve highly data and label efficient classifications.
Moreover, both elements are less sensitive to the prevalent and challenging
class imbalance issue, thanks to the (1) feature learned without labels and (2)
the Bayesian nature of GP. The GP-provided uncertainty estimates enable active
learning by ranking samples based on the uncertainty and selectively labeling
samples showing higher uncertainty. We apply this novel combination to the
severely imbalanced case of COVID-19 chest X-ray classification and the Nerthus
colonoscopy classification. We demonstrate that only . 10% of the labeled data
is needed to reach the accuracy from training all available labels. We also
applied our model architecture and proposed framework to a broader class of
datasets with expected success.
