We propose a simple yet novel active learning algorithm to support practitioners to acquire additional annotated CXR images efficiently.
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The results show that GOAL achieves full data training performance while using only $12.5\% \sim 34.1\%$ of the available annotated data. 
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Because only data points representing the informative region are sampled, the amount of data needed decreases dramatically.
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Furthermore, the usage of momentum helps stabilize the training by potentially removing the noisy label and keeping only stable and high confidence data points.
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We have not evaluated the proposed method on other public datasets in the general domain because the primary purpose of developing this is for the medical industry.
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In future work, we intend to study in-depth how the representation of deep CNN affects data choice and further evaluate our work on other domains.
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