CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification

TL;DR

This paper introduces Clinical, a targeted active learning framework that effectively addresses class imbalance in medical image classification by selecting critical rare class data points, outperforming existing methods.

Contribution

The paper proposes a novel active learning framework using submodular mutual information functions to improve rare class data acquisition in imbalanced medical datasets.

Findings

Clinical outperforms state-of-the-art active learning methods.

Effective in binary and long-tail imbalance scenarios.

Acquires diverse data points from rare classes.

Abstract

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.