Skip to main content
Top

Hint

Swipe to navigate through the chapters of this book

2021 | OriginalPaper | Chapter

Stain Mix-Up: Unsupervised Domain Generalization for Histopathology Images

Authors : Jia-Ren Chang, Min-Sheng Wu, Wei-Hsiang Yu, Chi-Chung Chen, Cheng-Kung Yang, Yen-Yu Lin, Chao-Yuan Yeh

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

Publisher: Springer International Publishing

share
SHARE

Abstract

Computational histopathology studies have shown that stain color variations considerably hamper the performance. Stain color variations indicate the slides exhibit greatly different color appearance due to the diversity of chemical stains, staining procedures, and slide scanners. Previous approaches tend to improve model robustness via data augmentation or stain color normalization. However, they still suffer from generalization to new domains with unseen stain colors. In this study, we address the issue of unseen color domain generalization in histopathology images by encouraging the model to adapt varied stain colors. To this end, we propose a novel data augmentation method, stain mix-up, which incorporates the stain colors of unseen domains into training data. Unlike previous mix-up methods employed in computer vision, the proposed method constructs the combination of stain colors without using any label information, hence enabling unsupervised domain generalization. Extensive experiments are conducted and demonstrate that our method is general enough to different tasks and stain methods, including H&E stains for tumor classification and hematological stains for bone marrow cell instance segmentation. The results validate that the proposed stain mix-up can significantly improves the performance on the unseen domains.
Appendix
Available only for authorised users
Literature
1.
go back to reference Bandi, P., et al.: From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge. IEEE Trans. Med. Imaging 38(2), 550–560 (2018) CrossRef Bandi, P., et al.: From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge. IEEE Trans. Med. Imaging 38(2), 550–560 (2018) CrossRef
2.
go back to reference Beer, A., Beer, P.: Determination of the absorption of red light in colored liquids. Annalen der Physik und Chemie 86(5), 78–88 (1852) CrossRef Beer, A., Beer, P.: Determination of the absorption of red light in colored liquids. Annalen der Physik und Chemie 86(5), 78–88 (1852) CrossRef
4.
go back to reference He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2961–2969 (2017) He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2961–2969 (2017)
5.
go back to reference He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
6.
go back to reference He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 558–567 (2019) He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 558–567 (2019)
8.
go back to reference Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2019) Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2019)
10.
go back to reference Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1107–1110. IEEE (2009) Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1107–1110. IEEE (2009)
11.
13.
go back to reference Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Gr. Appl. 21(5), 34–41 (2001) CrossRef Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Gr. Appl. 21(5), 34–41 (2001) CrossRef
14.
go back to reference Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001) Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001)
16.
go back to reference Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: Staingan: stain style transfer for digital histological images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (Isbi 2019), pp. 953–956. IEEE (2019) Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: Staingan: stain style transfer for digital histological images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (Isbi 2019), pp. 953–956. IEEE (2019)
17.
go back to reference 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) CrossRef 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) CrossRef
18.
go back to reference Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35(8), 1962–1971 (2016) CrossRef Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35(8), 1962–1971 (2016) CrossRef
19.
go back to reference Veta, M., Van Diest, P.J., Jiwa, M., Al-Janabi, S., Pluim, J.P.: Mitosis counting in breast cancer: object-level interobserver agreement and comparison to an automatic method. PloS one 11(8), e0161286 (2016) Veta, M., Van Diest, P.J., Jiwa, M., Al-Janabi, S., Pluim, J.P.: Mitosis counting in breast cancer: object-level interobserver agreement and comparison to an automatic method. PloS one 11(8), e0161286 (2016)
20.
go back to reference Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. In: International Conference on Learning Representations (2018) Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. In: International Conference on Learning Representations (2018)
Metadata
Title
Stain Mix-Up: Unsupervised Domain Generalization for Histopathology Images
Authors
Jia-Ren Chang
Min-Sheng Wu
Wei-Hsiang Yu
Chi-Chung Chen
Cheng-Kung Yang
Yen-Yu Lin
Chao-Yuan Yeh
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-87199-4_11

Premium Partner