Abstract
Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks’ generalization performance. In medical image analysis, a well-designed augmentation policy usually requires much expert knowledge and is difficult to generalize to multiple tasks due to the vast discrepancies among pixel intensities, image appearances, and object shapes in different medical tasks. To automate medical data augmentation, we propose a regularized adversarial training framework via two min-max objectives and three differentiable augmentation models covering affine transformation, deformation, and appearance changes. Our method is more automatic and efficient than previous automatic augmentation methods, which still rely on pre-defined operations with human-specified ranges and costly bi-level optimization. Extensive experiments demonstrated that our approach, with less training overhead, achieves superior performance over state-of-the-art auto-augmentation methods on both tasks of 2D skin cancer classification and 3D organs-at-risk segmentation.
This research was supported in part by NSF: IIS 1703883, NSF IUCRC CNS-1747778 and funding from SenseBrain, CCF-1733843, IIS-1763523, IIS-1849238, MURI- Z8424104 -440149 and NIH: 1R01HL127661-01 and R01HL127661-05. and in part by Centre for Perceptual and Interactive Intelligence (CPII) Limited, Hong Kong SAR.
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Gao, Y., Tang, Z., Zhou, M., Metaxas, D. (2021). Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds) Information Processing in Medical Imaging. IPMI 2021. Lecture Notes in Computer Science(), vol 12729. Springer, Cham. https://doi.org/10.1007/978-3-030-78191-0_7
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