Abstract
Cervical cancer is a significant health burden worldwide, and computer-aided diagnosis (CAD) pipelines have the potential to improve diagnosis efficiency and treatment outcomes. However, traditional CAD pipelines have limitations due to the requirement of a detection model trained on a large annotated dataset, which can be expensive and time-consuming. They also have a clear performance limit and low data utilization efficiency. To address these issues, we introduce a two-stage detection-free pipeline, incorporating pooling transformer and MoCo pretraining strategies, that optimizes data utilization for whole slide images (WSIs) while relying solely on sample-level diagnosis labels for training. The experimental results demonstrate the effectiveness of our approach, with performance scaling up as the amount of data increases. Overall, our novel pipeline has the potential to fully utilize massive data in WSI classification and can significantly improve cancer diagnosis and treatment. By reducing the reliance on expensive data labeling and detection models, our approach could enable more widespread and cost-effective implementation of CAD pipelines in clinical settings. Our code and model is available at https://github.com/thebestannie/Detection-free-MICCAI2023.
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
Cao, L., et al.: A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening. Med. Image Anal. 73, 102197 (2021)
Chen, B., et al.: PSViT: better vision transformer via token pooling and attention sharing. arXiv preprint arXiv:2108.03428 (2021)
Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)
Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9640–9649 (2021)
Cheng, S., et al.: Robust whole slide image analysis for cervical cancer screening using deep learning. Nat. Commun. 12(1), 1–10 (2021)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Gultekin, M., Ramirez, P.T., Broutet, N., Hutubessy, R.: World health organization call for action to eliminate cervical cancer globally. Int. J. Gynecol. Cancer 30(4), 426–427 (2020)
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009 (2022)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
Koss, L.G.: The papanicolaou test for cervical cancer detection: a triumph and a tragedy. Jama 261(5), 737–743 (1989)
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Marin, D., Chang, J.H.R., Ranjan, A., Prabhu, A., Rastegari, M., Tuzel, O.: Token pooling in vision transformers. arXiv preprint arXiv:2110.03860 (2021)
Meng, Z., Zhao, Z., Li, B., Fei, S., Guo, L.: A cervical histopathology dataset for computer aided diagnosis of precancerous lesions. IEEE Trans. Med. Imaging 40(6), 1531–1541 (2021)
Nayar, R., Wilbur, D.C.: The Bethesda System for Reporting Cervical Cytology: Definitions, Criteria, and Explanatory Notes. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11074-5
Patel, M.M., Pandya, A.N., Modi, J.: Cervical pap smear study and its utility in cancer screening, to specify the strategy for cervical cancer control. National J. Commun. Med. 2(01), 49–51 (2011)
Qu, L., Luo, X., Liu, S., Wang, M., Song, Z.: DGMIL: distribution guided multiple instance learning for whole slide image classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13432, pp. 24–34. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16434-7_3
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 400–407 (1951)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wei, Z., Cheng, S., Liu, X., Zeng, S.: An efficient cervical whole slide image analysis framework based on multi-scale semantic and spatial deep features. arXiv preprint arXiv:2106.15113 (2021)
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018)
Ye, M., Zhang, X., Yuen, P.C., Chang, S.-F.: Unsupervised embedding learning via invariant and spreading instance feature. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6210–6219 (2019)
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)
Zhang, X., et al.: Whole slide cervical cancer screening using graph attention network and supervised contrastive learning. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13432, pp. 202–211. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16434-7_20
Zhou, M., Zhang, L., Xiaping, D., Ouyang, X., Zhang, X., Shen, Q., Luo, D., Fan, X., Wang, Q.: Hierarchical pathology screening for cervical abnormality. Comput. Med. Imaging Graph. 89, 101892 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cao, M., Fei, M., Cai, J., Liu, L., Zhang, L., Wang, Q. (2023). Detection-Free Pipeline for Cervical Cancer Screening of Whole Slide Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-43987-2_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43986-5
Online ISBN: 978-3-031-43987-2
eBook Packages: Computer ScienceComputer Science (R0)
