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Targeted Active Learning using Submodular Mutual Information for Imbalanced Medical Image Classification
Suraj Kothawade · Lakshman Tamil · Rishabh Iyer

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 a framework for targeted active learning that uses submodular mutual information functions as acquisition functions. We show that Tailsman outperforms the state-of-the-art active learning methods by ~10%-12% on the rare classes accuracy and ~4%-6% on overall accuracy for Path-MNIST and Pneumonia-MNIST image classification datasets.

Author Information

Suraj Kothawade (University of Texas at Dallas)
Lakshman Tamil (The University of Texas at Dallas)
Rishabh Iyer (University of Texas, Dallas)

Bio: Prof. Rishabh Iyer is currently an Assistant Professor at the University of Texas, Dallas, where he leads the CARAML Lab. He is also a Visiting Assistant Professor at the Indian Institute of Technology, Bombay. He completed his Ph.D. in 2015 from the University of Washington, Seattle. He is excited in making ML more efficient (both computational and labeling efficiency), robust, and fair. He has received the best paper award at Neural Information Processing Systems (NeurIPS/NIPS) in 2013, the International Conference of Machine Learning (ICML) in 2013, and an Honorable Mention at CODS-COMAD in 2021. He has also won a Microsoft Research Ph.D. Fellowship, a Facebook Ph.D. Fellowship, and the Yang Award for Outstanding Graduate Student from the University of Washington.

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