Abstract
Thyroid nodule classification aims at determining whether the nodule is benign or malignant based on a given ultrasound image. However, the label obtained by the cytological biopsy which is the golden standard in clinical medicine is not always consistent with the ultrasound imaging TI-RADS criteria. The information difference between the two causes the existing deep learning-based classification methods to be indecisive. To solve the Inconsistent Label problem, we propose an Adaptive Curriculum Learning (ACL) framework, which adaptively discovers and discards the samples with inconsistent labels. Specifically, ACL takes both hard sample and model certainty into account, and could accurately determine the threshold to distinguish the samples with Inconsistent Label. Moreover, we contribute TNCD: a Thyroid Nodule Classification Dataset to facilitate future related research on the thyroid nodules. Extensive experimental results on TNCD based on three different backbone networks not only demonstrate the superiority of our method but also prove that the less-is-more principle which strategically discards the samples with Inconsistent Label could yield performance gains. Source code and data are available at https://github.com/chenghui-666/ACL/.
H. Gong and H. Cheng—Contribute equally to this work.
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References
Avola, D., Cinque, L., Fagioli, A., Filetti, S., Grani, G., Rodolà, E.: Multimodal feature fusion and knowledge-driven learning via experts consult for thyroid nodule classification. IEEE Trans. Circ. Syst. Video Technol. 32, 2527–2534 (2021)
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Danyluk, A.P., Bottou, L., Littman, M.L. (eds.) Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, 14–18 June 2009. ACM International Conference Proceeding Series, vol. 382, pp. 41–48. ACM (2009)
Castells, T., Weinzaepfel, P., Revaud, J.: SuperLoss: a generic loss for robust curriculum learning. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6–12 December 2020, virtual (2020)
Chen, J., You, H., Li, K.: A review of thyroid gland segmentation and thyroid nodule segmentation methods for medical ultrasound images. Comput. Methods Programs Biomed. 185, 105329 (2020)
Gong, H., et al.: Multi-task learning for thyroid nodule segmentation with thyroid region prior. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 257–261. IEEE (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 770–778 (2016)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 2261–2269 (2017)
Liu, J., Li, R., Sun, C.: Co-correcting: noise-tolerant medical image classification via mutual label correction. IEEE Trans. Med. Imaging 40(12), 3580–3592 (2021)
Liu, T., et al.: Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Medical Image Anal. 58, 101555 (2019)
Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)
Lyu, Y., Tsang, I.W.: Curriculum loss: robust learning and generalization against label corruption. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26–30 April 2020 (2020)
Paschke, R., Cantara, S., Crescenzi, A., Jarzab, B., Musholt, T.J., Simoes, M.S.: European thyroid association guidelines regarding thyroid nodule molecular fine-needle aspiration cytology diagnostics. Eur. Thyroid J. 6(3), 115–129 (2017)
Platanios, E.A., Stretcu, O., Neubig, G., Póczos, B., Mitchell, T.M.: Competence-based curriculum learning for neural machine translation. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, 2–7 June 2019, Minneapolis, MN, USA, vol. 1, pp. 1162–1172. Association for Computational Linguistics (2019)
Song, R., Zhang, L., Zhu, C., Liu, J., Yang, J., Zhang, T.: Thyroid nodule ultrasound image classification through hybrid feature cropping network. IEEE Access 8, 64064–64074 (2020)
Tessler, F.N., et al.: ACR thyroid imaging, reporting and data system (TI-RADS): white paper of the ACR TI-RADS committee. J. Am. Coll. Radiol. 14(5), 587–595 (2017)
Wang, L., Zhang, L., Zhu, M., Qi, X., Yi, Z.: Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Medical Image Anal. 61, 101665 (2020)
Wang, Y., Gan, W., Yang, J., Wu, W., Yan, J.: Dynamic curriculum learning for imbalanced data classification. In: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), 27 October–2 November 2019, pp. 5016–5025. IEEE (2019)
Yang, W., et al.: Integrate domain knowledge in training multi-task cascade deep learning model for benign-malignant thyroid nodule classification on ultrasound images. Eng. Appl. Artif. Intell. 98, 104064 (2021)
Zhao, S.X., Chen, Y., Yang, K.F., Luo, Y., Ma, B.Y., Li, Y.J.: A local and global feature disentangled network: toward classification of benign-malignant thyroid nodules from ultrasound image. IEEE Trans. Med. Imaging (2022)
Acknowledgement
This work is supported in part by the Chinese Key-Area Research and Development Program of Guangdong Province (2020B0101350001), in part by the Guangdong Basic and Applied Basic Research Foundation (2020B1515020048), in part by the National Natural Science Foundation of China (61976250), in part by the Guangzhou Science and technology project (No. 202102020633), and in part by the Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen.
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Gong, H. et al. (2022). Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_24
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