Abstract
The superior performance of CNN on medical image analysis heavily depends on the annotation quality, such as the number of labeled images, the source of images, and the expert experience. The annotation requires great expertise and labor. To deal with the high inter-rater variability, the study of the imperfect label has great significance in medical image segmentation tasks. In this paper, we present a novel cascaded robust learning framework for chest X-ray segmentation with imperfect annotation at the boundary. Our model consists of three independent networks, which can effectively learn useful information from peer networks. The framework includes two stages. In the first stage, we select the clean annotated samples via a model committee setting, the networks are trained by minimizing a segmentation loss using the selected clean samples. In the second stage, we design a joint optimization framework with label correction to gradually correct the wrong annotation and improve the network performance. We conduct experiments on the public chest X-ray image datasets collected by Shenzhen Hospital. The results show that our methods could achieve a significant improvement on the accuracy in segmentation tasks compared to the previous methods.
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
Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 (2017)
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: MixMatch: a holistic approach to semi-supervised learning. In: Advances in Neural Information Processing Systems, pp. 5050–5060 (2019)
Candemir, S., et al.: Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med. Imaging 33(2), 577–590 (2013)
De Fauw, J., et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24(9), 1342–1350 (2018)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)
Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer. In: ICLR (2017)
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)
Jaeger, S., et al.: Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med. Imaging 33(2), 233–245 (2013)
Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: regularizing very deep neural networks on corrupted labels. In: ICML (2018)
Le, H., Samaras, D., Kurc, T., Gupta, R., Shroyer, K., Saltz, J.: Pancreatic cancer detection in whole slide images using noisy label annotations. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 541–549. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_60
Li, X., Yu, L., Chen, H., Fu, C.W., Xing, L., Heng, P.A.: Transformation-consistent self-ensembling model for semisupervised medical image segmentation. IEEE Trans. Neural Netw. Learn. Syst. (2020)
Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. In: ICML (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Setio, A.A.A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1–13 (2017)
Shu, Y., Wu, X., Li, W.: LVC-Net: medical image segmentation with noisy label based on local visual cues. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 558–566. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_62
Stirenko, S., et al.: Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation. In: 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO), pp. 422–428. IEEE (2018)
Xue, C., Dou, Q., Shi, X., Chen, H., Heng, P.A.: Robust learning at noisy labeled medical images: applied to skin lesion classification. In: ISBI (2019)
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. In: ICLR (2017)
Zhu, H., Shi, J., Wu, J.: Pick-and-learn: automatic quality evaluation for noisy-labeled image segmentation. arXiv preprint arXiv:1907.11835 (2019)
Acknowledgments
This work is supported by Hong Kong Innovation and Technology Commission (Project No. ITS/311/18FP), Shenzhen Science and Technology Program (JCYJ20170413162256793) and a CUHK Direct Grant for Research.
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
Xue, C., Deng, Q., Li, X., Dou, Q., Heng, PA. (2020). Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-59725-2_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59724-5
Online ISBN: 978-3-030-59725-2
eBook Packages: Computer ScienceComputer Science (R0)
