Abstract
Deep learning have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole slide images and pathologists’ heavy daily workload, obtaining pixel-level labels for supervised learning in clinical practice is often infeasible. Alternatively, weakly-supervised segmentation methods have been explored with less laborious image-level labels, but their performance is unsatisfactory due to the lack of dense supervision. Inspired by the recent success of self-supervised learning, we present a label-efficient tissue prototype dictionary building pipeline and propose to use the obtained prototypes to guide histopathology image segmentation. Particularly, taking advantage of self-supervised contrastive learning, an encoder is trained to project the unlabeled histopathology image patches into a discriminative embedding space where these patches are clustered to identify the tissue prototypes by efficient pathologists’ visual examination. Then, the encoder is used to map the images into the embedding space and generate pixel-level pseudo tissue masks by querying the tissue prototype dictionary. Finally, the pseudo masks are used to train a segmentation network with dense supervision for better performance. Experiments on two public datasets demonstrate that our method can achieve comparable segmentation performance as the fully-supervised baselines with less annotation burden and outperform other weakly-supervised methods. Codes are available at https://github.com/WinterPan2017/proto2seg.
W. Pan, J. Yan and H. Chen—contributed equally.
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
Similar content being viewed by others
References
Amgad, M., Elfandy, H., et al.: Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics 35(18), 3461–3467 (2019)
Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: SODA, pp. 1027–1035 (2007)
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)
Chan, L., Hosseini, M.S., Rowsell, C., et al.: HistoSegNet: semantic segmentation of histological tissue type in whole slide images. In: ICCV, pp. 10662–10671 (2019)
Chattopadhay, A., Sarkar, A., et al.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: WACV, pp. 839–847 (2018)
Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: VCIP, pp. 1–4 (2017)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML, pp. 1597–1607 (2020)
Ester, M., Kriegel, H.P., Sander, J., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)
Grill, J.B., Strub, F., Altché, F., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: NeurIPS, pp. 21271–21284 (2020)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR, pp. 9729–9738 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: ICML, pp. 2127–2136 (2018)
Liu, F., Deng, Y.: Determine the number of unknown targets in open world based on elbow method. IEEE Trans. Fuzzy Syst. 29(5), 986–995 (2020)
Lu, M.Y., et al.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555–570 (2021)
Sarfraz, S., Sharma, V., Stiefelhagen, R.: Efficient parameter-free clustering using first neighbor relations. In: CVPR, pp. 8934–8943 (2019)
Selvaraju, R.R., Cogswell, M., Das, A., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: ICCV, pp. 618–626 (2017)
Xu, G., Song, Z., Sun, Z., et al.: CAMEL: a weakly supervised learning framework for histopathology image segmentation. In: CVPR, pp. 10682–10691 (2019)
Xu, Z., Lu, D., Luo, J., et al.: Anti-interference from noisy labels: mean-teacher-assisted confident learning for medical image segmentation. IEEE Trans. Med. Imaging 41(11), 3062–3073 (2022)
Xu, Z., et al.: Noisy labels are treasure: mean-teacher-assisted confident learning for hepatic vessel segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 3–13. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_1
Xu, Z., et al.: Denoising for relaxing: unsupervised domain adaptive fundus image segmentation without source data. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 214–224. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16443-9_21
Yan, J., Chen, H., Li, X., Yao, J.: Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis. Comput. Med. Imaging Graph. 97, 102053 (2022)
Yang, J., et al.: Towards better understanding and better generalization of low-shot classification in histology images with contrastive learning. In: ICLR (2022)
Yang, P., Hong, Z., Yin, X., Zhu, C., Jiang, R.: Self-supervised visual representation learning for histopathological images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 47–57. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_5
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR, pp. 2921–2929 (2016)
Acknowledgement
This research was partly supported by the National Key R &D Program of China (Grant No. 2020AAA0108303), Shenzhen Science and Technology Project (Grant No. JCYJ20200109143041798), Shenzhen Stable Supporting Program (Grant No. WDZC20200820200655001), and Shenzhen Key Lab of next generation interactive media innovative technology (Grant No. ZDSY S20210623092001004).
Author information
Authors and Affiliations
Corresponding authors
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
Pan, W. et al. (2023). Human-Machine Interactive Tissue Prototype Learning for Label-Efficient Histopathology Image Segmentation. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds) Information Processing in Medical Imaging. IPMI 2023. Lecture Notes in Computer Science, vol 13939. Springer, Cham. https://doi.org/10.1007/978-3-031-34048-2_52
Download citation
DOI: https://doi.org/10.1007/978-3-031-34048-2_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34047-5
Online ISBN: 978-3-031-34048-2
eBook Packages: Computer ScienceComputer Science (R0)