Abstract
Accurate recognition of anatomy sites is important for evaluating the quality of esophagogastroduodenoscopy (EGD) examinations. However, because some anatomy sites have similar appearances and anatomical landmarks are lacking, gastric-anatomy image annotations are less than accurate. The annotations by doctors with various experience levels vary widely. Deep learning–based systems trained on these noisy annotations have poor recognition performance. In this work, we propose a novel data refinement approach to alleviate the problem of noisy annotations and improve the upper gastrointestinal anatomy recognition performance. In essence, we introduce a new uncertainty inference module for deep convolutional neural networks (CNNs) and leverage Bayesian uncertainty estimates to select possibly noisy data. In addition, we employ an ensemble of semi-supervised learning to rectify noisy labels and produce refined training data. We validate the proposed approach via controlled experiments on CIFAR-10, in which the noise rate is adjusted and noisy data are made known. It shows much improvement on classification accuracy using the refined dataset, and outperforms state-of-the-art robust training methods, e.g., MentorNet and Co-teaching. An evaluation of the upper gastrointestinal anatomy recognition task proves that our proposed method effectively improves the recognition accuracy for real, noisy clinical data. The proposed data refinement approach reduces the human effort needed to filter out and manually rectify noisy annotations. It can also be applied to wider scenarios where accurate expert labeling is expensive.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhu, R., Zhang, R., Xue, D.: Lesion detection of endoscopy images based on convolutional neural network features. In: 2015 8th International Congress on Image and Signal Processing, pp. 372–376 (2015)
Zhang, C., Recht, B., Bengio, S., Hardt, M., Vinyals, O.: Understanding deep learning requires rethinking generalization. In: 5th International Conference on Learning Representations (2017)
Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., Li, L.J.: Learning from noisy labels with distillation. In: International Conference on Computer Vision, pp. 1928–1936 (2017)
Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., Belongie, S.: Learning from noisy large-scale datasets with minimal supervision. In: 30th IEEE Conference on Computer Vision and Pattern Recognition, pp. 6575–6583 (2017)
Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels. In: 35th International Conference on Machine Learning, pp. 2304–2313 (2018)
Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems, pp. 8527–8537 (2018)
Li, J., Wong, Y., Zhao, Q., Kankanhalli, M.S.: Learning to learn from noisy labeled data. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5051–5059 (2019)
Khan, S., Hayat, M., Zamir, S.W., Shen, J., Shao, L.: Striking the right balance with uncertainty. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 103–112 (2019)
Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian SegNet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. In: British Machine Vision Conference (2017)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: 33rd International Conference on Machine Learning, pp. 1050–1059 (2016)
Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. In: Advances in Neural Information Processing Systems, pp. 2575–2583 (2015)
Houlsby, N., Husz, F., Ghahramani, Z., Lengyel, M.: Bayesian active learning for classification and preference learning. Comput. Sci. 10–13 (2011)
Gal, Y., Islam, R., Ghahramani, Z.: Deep Bayesian active learning with image data. In: 34th International Conference on Machine Learning, pp. 1183–1192 (2017)
Schmarje, L., Santarossa, M., Schr, S., Koch, R.: A survey on semi-, self-and unsupervised techniques in image classification similarities, differences & combinations. arXiv:2002.08721 (2020)
Miyato, T., Maeda, S.I., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1979–1993 (2019)
Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Sch. Comput Science Carnegie Mellon University Technical report CMUCALD02107 (2002)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Zhang, Z., Sabuncu, M.R.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: Neural Information Processing Systems, pp. 8792–8802 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Quan, L., Li, Y., Chen, X., Zhang, N. (2020). An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition. 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_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-59710-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59709-2
Online ISBN: 978-3-030-59710-8
eBook Packages: Computer ScienceComputer Science (R0)
