Abstract
Deep learning-based automatic medical diagnosis is intensively studied in recent years. Abundant clinical raw records can be utilized, but we demonstrate that mixed and unknown magnification scales and staining conditions of raw histopathology images greatly hinder many successful deep models in this task. To address this problem, this paper proposes an Online Adaptive Data Augmentation method (OADA). In each training epoch, OADA adaptively selects base images and determines the personalized augmentation size of each image based on the current training status. The chosen images are augmented to update the training set. Extensive experiments show that OADA-empowered deep models obtain significant improvement compared to their bare versions, and OADA outperforms a suite of data augmentation baselines and state-of-the-art competitors.
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References
Coudray, N., Moreira, A.L., Sakellaropoulos, T., Feny, D., Tsirigos, A.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018)
Hashimoto, N., et al.: Multi-scale domain-adversarial multiple-instance CNN for cancer subtype classification with unannotated histopathological images. In: CVPR, pp. 3851–3860. IEEE (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778. IEEE (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, pp. 4700–4708 (2017)
Inoue, H.: Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929 (2018)
Li, S., Chen, Y., Peng, Y., Bai, L.: Learning more robust features with adversarial training. arXiv preprint arXiv:1804.07757 (2018)
Mounsaveng, S., Laradji, I.H., Ayed, I.B., Vázquez, D., Pedersoli, M.: Learning data augmentation with online bilevel optimization for image classification. In: WACV, pp. 1690–1699. IEEE (2021)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) ICLR (2015)
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016)
Tang, Z., Gao, Y., Karlinsky, L., Sattigeri, P., Feris, R., Metaxas, D.: OnlineAugment: online data augmentation with less domain knowledge. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 313–329. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58571-6_19
Taylor, L., Nitschke, G.: Improving deep learning using generic data augmentation. arXiv preprint arXiv:1708.06020 (2017)
Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)
Wei, J.W., Tafe, L.J., Linnik, Y.A., Vaickus, L.J., Tomita, N., Hassanpour, S.: Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci. Rep. 9(1), 1–8 (2019)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp. 2223–2232 (2017)
Acknowledgements
This work is supported by the National Key Research and Development Program of China (2016YFB1000101), the National Natural Science Foundation of China (No. 61379052), the Science Foundation of Ministry of Education of China (No. 2018A02002), and the Natural Science Foundation for Distinguished Young Scholars of Hunan Province (No. 14JJ1026).
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Wu, Z., Wang, Y., Mi, H., Xu, H., Zhang, W., Feng, L. (2021). OADA: An Online Data Augmentation Method for Raw Histopathology Images. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_29
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DOI: https://doi.org/10.1007/978-3-030-92310-5_29
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