Abstract
Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images. Thus, the effectiveness of those synthetic images in medical image recognition systems cannot be guaranteed when they are being added randomly without quality assurance. In this work, we propose a reinforcement learning (RL) based synthetic sample selection method that learns to choose synthetic images containing reliable and informative features. A transformer based controller is trained via proximal policy optimization (PPO) using the validation classification accuracy as the reward. The selected images are mixed with the original training data for improved training of image recognition systems. To validate our method, we take the pathology image recognition as an example and conduct extensive experiments on two histopathology image datasets. In experiments on a cervical dataset and a lymph node dataset, the image classification performance is improved by \(8.1\%\) and \(2.3\%\), respectively, when utilizing high-quality synthetic images selected by our RL framework. Our proposed synthetic sample selection method is general and has great potential to boost the performance of various medical image recognition systems given limited annotation.
J. Ye and Y. Xue—These authors contributed equally to this work.
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Acknowledgements
This work was supported in part by the Intramural Research Program of the National Library of Medicine and the National Institutes of Health. We gratefully acknowledge the help with expert annotations from Dr. Rosemary Zuna, M.D., of the University of Oklahoma Health Sciences Center, and the work of Dr. Joe Stanley of Missouri University of Science and Technology that made the histopathology data collection possible.
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Ye, J. et al. (2020). Synthetic Sample Selection via Reinforcement Learning. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_6
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