Relieving pixel-wise labeling effort for pathology image segmentation with self-training

[en] Data scarcity is a common issue when training deep learning models for digital pathology, as large exhaustively-annotated image datasets are difficult to obtain. In this paper, we propose a self-training based approach that can exploit both (few) exhaustively annotated images and (very) sparsely-annotated images to improve the training of deep learning models for image segmentation tasks. The approach is evaluated on three public and one in-house datasets, representing a diverse set of segmentation tasks in digital pathology. The experimental results show that self-training allows to bring significant model improvement by incorporating sparsely annotated images and proves to be a good strategy to relieve labeling effort in the digital pathology domain.

Disciplines :

Electrical & electronics engineering

Author, co-author :

Mormont, Romain ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science

Testouri, Mehdi; Unilu - Université du Luxembourg [LU] > Interdisciplinary Centre for Security, Reliability and Trust > Services and Data Management (SEDAN)

Marée, Raphaël ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science

Geurts, Pierre ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science

Language :

English

Title :

Relieving pixel-wise labeling effort for pathology image segmentation with self-training

Alternative titles :

[fr] Utilisation de l'auto-apprentissage pour réduire le coût d'annotation pour la segmentation d'image en pathology digitale

Publication date :

October 2022

Event name :

European Conference in Compter Vision (ECCV2022)

Event organizer :

European Computer Vision Association

Event place :

Tel Aviv, Israel

Event date :

October 23, 2022

Audience :

International

Main work title :

Lecture Notes in Computer Science

Publisher :

Springer Cham, Genève, Switzerland

Peer reviewed :

Peer reviewed

Available on ORBi :

since 17 August 2022

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Bibliography

Bai, W., et al.: Semi-supervised learning for network-based cardiac MR image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 253–260. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8 29
Bankhead, P., et al.: QuPath: open source software for digital pathology image analysis. Sci. Rep. 7(1), 1–7 (2017)
Berg, S., et al.: ilastik: interactive machine learning for (bio)image analysis. Nat. Methods (2019). https://doi.org/10.1038/s41592-019-0582-9
Bokhorst, J.M., Pinckaers, H., van Zwam, P., Nagtegaal, I., van der Laak, J., Ciompi, F.: Learning from sparsely annotated data for semantic segmentation in histopathology images. In: International Conference on Medical Imaging with Deep Learning-Full Paper Track (2018)
Fan, D.P., et al.: Inf-net: automatic Covid-19 lung infection segmentation from CT images. IEEE Trans. Med. Imaging 39(8), 2626–2637 (2020)
Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in Neural Information Processing Systems, vol. 17 (2004)
Gupta, A., Gupta, R., Gehlot, S., Goswami, S.: SegPC-2021: segmentation of multiple myeloma plasma cells in microscopic images. IEEE Dataport 1(1), 1 (2021)
Jaiswal, A.K., Panshin, I., Shulkin, D., Aneja, N., Abramov, S.: Semisupervised learning for cancer detection of lymph node metastases. arXiv preprint arXiv:1906.09587 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Koohbanani, N.A., Jahanifar, M., Tajadin, N.Z., Rajpoot, N.: NuClick: a deep learning framework for interactive segmentation of microscopic images. Med. Image Anal. 65, 101771 (2020)
Koohbanani, N.A., Unnikrishnan, B., Khurram, S.A., Krishnaswamy, P., Rajpoot, N.: Self-path: self-supervision for classification of pathology images with limited annotations. IEEE Trans. Med. Imaging 40(10), 2845–2856 (2021)
Kumar, N., et al.: A multi-organ nucleus segmentation challenge. IEEE Trans. Med. Imaging 39(5), 1380–1391 (2019)
Kwatra, V., Schödl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: image and video synthesis using graph cuts. ACM Trans. Graph. (TOG) 22(3), 277–286 (2003)
Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (2016)
Lee, D.H., et al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML, vol. 3, p. 896 (2013)
Li, J., et al.: Signet ring cell detection with a semi-supervised learning framework. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 842–854. Springer, Cham (2019). https://doi.org/10.1007/978-3030-20351-1 66
Li, J., et al.: An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies. Comput. Med. Imaging Graph. 69, 125–133 (2018)
Maninis, K.K., Caelles, S., Pont-Tuset, J., Van Gool, L.: Deep extreme cut: from extreme points to object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 616–625 (2018)
Marée, R., et al.: Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics 32(9), 1395–1401 (2016)
Peikari, M., Salama, S., Nofech-Mozes, S., Martel, A.L.: A cluster-then-label semisupervised learning approach for pathology image classification. Sci. Rep. 8(1), 1–13 (2018)
Peng, J., Wang, Y.: Medical image segmentation with limited supervision: a review of deep network models. IEEE Access (2021)
Pham, H., Dai, Z., Xie, Q., Le, Q.V.: Meta pseudo labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11557– 11568 (2021)
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
Rother, C., Kolmogorov, V., Blake, A.: “GrabCut” interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. (TOG) 23(3), 309–314 (2004)
Shaw, S., Pajak, M., Lisowska, A., Tsaftaris, S.A., O’Neil, A.Q.: Teacher-student chain for efficient semi-supervised histology image classification. arXiv preprint arXiv:2003.08797 (2020)
Sirinukunwattana, K., et al.: Gland segmentation in colon histology images: the GlaS challenge contest. Med. Image Anal. 35, 489–502 (2017)
Sohn, K., et al.: FixMatch: simplifying semi-supervised learning with consistency and confidence. In: Advances in Neural Information Processing Systems, vol. 33, pp. 596–608 (2020)
Su, H., Shi, X., Cai, J., Yang, L.: Local and global consistency regularized mean teacher for semi-supervised nuclei classification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 559–567. Springer, Cham (2019). https://doi.org/10. 1007/978-3-030-32239-7 62
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J.N., Wu, Z., Ding, X.: Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med. Image Anal. 63, 101693 (2020)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Tellez, D., et al.: Whole-slide mitosis detection in H&E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks. IEEE Trans. Med. Imaging 37(9), 2126–2136 (2018)
Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10687–10698 (2020)
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189–196 (1995)
Zhu, Y., et al.: Improving semantic segmentation via self-training. arXiv preprint arXiv:2004.14960 (2020)