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Training robust deep learning (DL) systems for medical image classificationor segmentation is challenging due to limited images covering different diseasetypes and severity. We propose an active learning (AL) framework to select mostinformative samples and add to the training data. We use conditional generativeadversarial networks (cGANs) to generate realistic chest xray images withdifferent disease characteristics by conditioning its generation on a realimage sample. Informative samples to add to the training set are identifiedusing a Bayesian neural network. Experiments show our proposed AL framework isable to achieve state of the art performance by using about 35% of the fulldataset, thus saving significant time and effort over conventional methods.

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network