Abstract
Thyroid cytology whole slide images (WSIs) hold vital information essential for precise diagnosis. Given the huge size of WSIs, multiple instance learning (MIL) is an effective solution for the WSI classification task when only slide-level labels are accessible. The embedding-based MIL uses a feature extractor pretrained with the self-supervised contrastive learning framework to eliminate the dependence on patch-level labels. However, the distribution of class in thyroid patches is unbalanced, and most existing self-supervised contrastive learning methods take little account of the data imbalance, which makes the features not discriminative enough. To address this problem, we propose a novel balanced self-supervised contrastive learning (BS2CL) framework for pretraining. It first clusters the patches to preserve the class structure of the patches and then assigns the clustering centers to a set of pre-computed uniformly distributed optimal locations. This constraint creates a more uniform distribution of different classes in the feature space which leads to clearer class boundaries between different classes, an unbiased feature space, and more discriminative features. Furthermore, a bag-level data augmentation strategy is introduced to increase bag quantities and improve classification performance. Extensive experiments show that the proposed method outperforms other latest methods on the thyroid cytology WSI dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cabanillas, M.E., McFadden, D.G., Durante, C.: Thyroid cancer. The Lancet 388(10061), 2783–2795 (2016)
Haugen, B.R., Alexander, E.K., Bible, K.C., et al.: 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid 26(1), 1–133 (2016)
Cibas, E.S., Ali, S.Z.: The 2017 Bethesda system for reporting thyroid cytopathology. Thyroid 27(11), 1341–1346 (2017)
Cochand-Priollet, B., Koutroumbas, K., Megalopoulou, T.M., et al.: Discriminating benign from malignant thyroid lesions using artificial intelligence and statistical selection of morphometric features. Oncol. Rep. 15(4), 1023–1026 (2006)
Gopinath, B., Shanthi, N.: Development of an automated medical diagnosis system for classifying thyroid tumor cells using multiple classifier fusion. Technol. Cancer Res. Treat. 14(5), 653–662 (2015)
Chain, K., Legesse, T., Heath, J.E., et al.: Digital image-assisted quantitative nuclear analysis improves diagnostic accuracy of thyroid fine-needle aspiration cytology. Cancer Cytopathol. 127(8), 501–513 (2019)
Guan, Q., Wang, Y., Ping, B., et al.: Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J. Cancer 10(20), 4876 (2019)
Hirokawa, M., Niioka, H., Suzuki, A., et al.: Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology. Cancer Cytopathol. 131(4), 217–225 (2023)
Dov, D., Kovalsky, S.Z., Assaad, S., et al.: Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images. Med. Image Anal. 67, 101814 (2021)
Qiu, S., Guo, Y., Zhu, C., et al.: Attention based multi-instance thyroid cytopathological diagnosis with multi-scale feature fusion. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3536–3541 (2021)
Yu, B., Yin, P., Chen, H., et al.: Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear. Knowl.-Based Syst. 275, 110721 (2023)
Feng, J., Zhou, Z.H.: Deep MIML network. In: Proceedings of the AAAI conference on artificial intelligence, pp. 1884–1890 (2017)
Pinheiro, P.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1713–1721 (2015)
Zhu, W., Lou, Q., Vang, Y.S., et al.: Deep multi-instance networks with sparse label assignment for whole mammogram classification. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 603–611. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_69
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127–2136 (2018)
Chen, T., Kornblith, S., Norouzi, M., et al.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020)
He, K., Fan, H., Wu, Y., et al.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
Caron, M., Touvron, H., Misra, I., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9650–9660 (2021)
Deng, J., Dong, W., Socher, R., et al.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Assran, M., Balestriero, R., Duval, Q., et al.: The hidden uniform cluster prior in self-supervised learning. arXiv preprint arXiv:2210.07277 (2022)
Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318–14328 (2021)
Lu, M.Y., Williamson, D.F., Chen, T.Y., et al.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555–570 (2021)
Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
Wu, Z., Xiong, Y., Yu, S.X., et al.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018)
Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Q 2(1–2), 83–97 (1955)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)
Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with GPUs. IEEE Trans. Big Data 7(3), 535–547 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. arXiv preprint arXiv:2104.02057 (2021)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Duan, W. et al. (2024). BS2CL: Balanced Self-supervised Contrastive Learning for Thyroid Cytology Whole Slide Image Multi-classification. In: Huang, DS., Zhang, C., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14868. Springer, Singapore. https://doi.org/10.1007/978-981-97-5600-1_4
Download citation
DOI: https://doi.org/10.1007/978-981-97-5600-1_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5599-8
Online ISBN: 978-981-97-5600-1
eBook Packages: Computer ScienceComputer Science (R0)