  Large, annotated datasets are not widely available in medical image analysis
due to the prohibitive time, costs, and challenges associated with labelling
large datasets. Unlabelled datasets are easier to obtain, and in many contexts,
it would be feasible for an expert to provide labels for a small subset of
images. This work presents an information-theoretic active learning framework
that guides the optimal selection of images from the unlabelled pool to be
labeled based on maximizing the expected information gain (EIG) on an
evaluation dataset. Experiments are performed on two different medical image
classification datasets: multi-class diabetic retinopathy disease scale
classification and multi-class skin lesion classification. Results indicate
that by adapting EIG to account for class-imbalances, our proposed Adapted
Expected Information Gain (AEIG) outperforms several popular baselines
including the diversity based CoreSet and uncertainty based maximum entropy
sampling. Specifically, AEIG achieves ~95% of overall performance with only 19%
of the training data, while other active learning approaches require around
25%. We show that, by careful design choices, our model can be integrated into
existing deep learning classifiers.
