  Training deep learning models on medical datasets that perform well for all
classes is a challenging task. It is often the case that a suboptimal
performance is obtained on some classes due to the natural class imbalance
issue that comes with medical data. An effective way to tackle this problem is
by using targeted active learning, where we iteratively add data points to the
training data that belong to the rare classes. However, existing active
learning methods are ineffective in targeting rare classes in medical datasets.
In this work, we propose Clinical (targeted aCtive Learning for ImbalaNced
medICal imAge cLassification) a framework that uses submodular mutual
information functions as acquisition functions to mine critical data points
from rare classes. We apply our framework to a wide-array of medical imaging
datasets on a variety of real-world class imbalance scenarios - namely, binary
imbalance and long-tail imbalance. We show that Clinical outperforms the
state-of-the-art active learning methods by acquiring a diverse set of data
points that belong to the rare classes.
