Abstract
Stain color variations between Whole Slide Images (WSIs) is a key challenge in the application of Computational Histopathology. Deep learning-based algorithms are susceptible to domain shift and degrade in performance on the WSIs captured from a different source than the training data due to stain color variations. We propose a training methodology Stain-AgLr, that achieves high invariance to stain color changes on unseen test data. In addition to task loss, Stain-AgLr training is supervised with a consistency regularization loss that enforces consistent predictions for training samples and their stain altered versions. An additional decoder is used to regenerate stain color from feature representation of the stain altered images. We compare the proposed approach to state-of-the-art strategies using two histopathology datasets and show significant improvement in model performance on unseen stain variations. We also visualize the feature space distribution of test samples from multiple diagnostic labs and show that Stain-AgLr achieves a significant overlap between the distributions.
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References
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)
Bejnordi, B.E., et al.: Stain specific standardization of whole-slide histopathological images. IEEE Trans. Med. Imaging 35(2), 404–415 (2015)
de Bel, T., Bokhorst, J.M., van der Laak, J., Litjens, G.: Residual cyclegan for robust domain transformation of histopathological tissue slides. Med. Image Anal. 70, 102004 (2021)
Bera, K., Schalper, K.A., Rimm, D.L., Velcheti, V., Madabhushi, A.: Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16(11), 703–715 (2019)
Bertram, C.A., et al.: Are pathologist-defined labels reproducible? comparison of the tupac16 mitotic figure dataset with an alternative set of labels. In: Cardoso, J., et al. (eds.) IMIMIC/MIL3ID/LABELS -2020. LNCS, vol. 12446, pp. 204–213. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61166-8_22
Chang, J.-R., et al.: Stain mix-up: Unsupervised domain generalization for histopathology images. In: de Chang, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 117–126. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_11
Cohen, J.P., Luck, M., Honari, S.: Distribution matching losses can hallucinate features in medical image translation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 529–536. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_60
Cong, C., Liu, S., Di Ieva, A., Pagnucco, M., Berkovsky, S., Song, Y.: Semi-supervised adversarial learning for stain normalisation in histopathology images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 581–591. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_56
Cui, M., Zhang, D.Y.: Artificial intelligence and computational pathology. Lab. Invest. 101(4), 412–422 (2021)
Faryna, K., van der Laak, J., Litjens, G.: Tailoring automated data augmentation to h &e-stained histopathology. In: Medical Imaging with Deep Learning (2021)
Koh, P.W., et al.: Wilds: a benchmark of in-the-wild distribution shifts. In: International Conference on Machine Learning, pp. 5637–5664. PMLR (2021)
Lafarge, M.W., Pluim, J.P.W., Eppenhof, K.A.J., Moeskops, P., Veta, M.: Domain-adversarial neural networks to address the appearance variability of histopathology images. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 83–91. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_10
Liang, H., Plataniotis, K.N., Li, X.: Stain style transfer of histopathology images via structure-preserved generative learning. In: Deeba, F., Johnson, P., Würfl, T., Ye, J.C. (eds.) MLMIR 2020. LNCS, vol. 12450, pp. 153–162. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61598-7_15
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)
Marini, N., Atzori, M., Otálora, S., Marchand-Maillet, S., Muller, H.: H &e-adversarial network: a convolutional neural network to learn stain-invariant features through hematoxylin & eosin regression. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 601–610 (2021)
Melas-Kyriazi, L., Manrai, A.K.: Pixmatch: Unsupervised domain adaptation via pixelwise consistency training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12435–12445 (2021)
Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graphics Appl. 21(5), 34–41 (2001)
Sainburg, T., McInnes, L., Gentner, T.Q.: Parametric umap: learning embeddings with deep neural networks for representation and semi-supervised learning. ArXiv e-prints (2020)
Salehi, P., Chalechale, A.: Pix2pix-based stain-to-stain translation: A solution for robust stain normalization in histopathology images analysis. In: 2020 International Conference on Machine Vision and Image Processing (MVIP), pp. 1–7. IEEE (2020)
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)
Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596–608 (2020)
Stacke, K., Eilertsen, G., Unger, J., Lundström, C.: A closer look at domain shift for deep learning in histopathology. arXiv preprint arXiv:1909.11575 (2019)
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)
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med. Image Anal. 58, 101544 (2019)
Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35(8), 1962–1971 (2016)
Vasiljević, J., Feuerhake, F., Wemmert, C., Lampert, T.: Towards histopathological stain invariance by unsupervised domain augmentation using generative adversarial networks. Neurocomputing 460, 277–291 (2021)
Wagner, S.J., et al.: Structure-Preserving Multi-domain Stain Color Augmentation Using Style-Transfer with Disentangled Representations. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 257–266. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_25
Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018)
Xie, Q., Dai, Z., Hovy, E., Luong, T., Le, Q.: Unsupervised data augmentation for consistency training. Adv. Neural. Inf. Process. Syst. 33, 6256–6268 (2020)
Zhang, Y., Zhang, H., Deng, B., Li, S., Jia, K., Zhang, L.: Semi-supervised models are strong unsupervised domain adaptation learners. arXiv preprint arXiv:2106.00417 (2021)
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Appendices
A Qualitative Results
See Fig. 4
Stain normalized samples for all subsets acquired at different labs. Data from Center 1 and RUMC is used as the training for TUPAC and CAMELYON respectively. Deep Learning based models, DCSCI GAN [13], STST GAN [19], and Stain-AgLr (Ours) learn stain color distribution from the entire training dataset; whereas traditional stain normalization methods including Vahadane [26] and Macenko [14] use a single image as reference, shown in the last row. Stain-CRS does not use stain normalized images, rather the normalized image is output of the stain regeneration auxiliary task.
B Model Architecture Details
See Table 3
C Weightage of Consistency Regularization and Regeneration Loss
See Fig. 5
Performance of the proposed model with different \(\lambda _1\) and \(\lambda _2\) values - weightage of Stain Regeneration and Consistency Regularization loss, keeping the other \(\lambda =0\), evaluated on Camelyon dataset
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Raipuria, G., Shrivastava, A., Singhal, N. (2022). Stain-AgLr: Stain Agnostic Learning for Computational Histopathology Using Domain Consistency and Stain Regeneration Loss. In: Kamnitsas, K., et al. Domain Adaptation and Representation Transfer. DART 2022. Lecture Notes in Computer Science, vol 13542. Springer, Cham. https://doi.org/10.1007/978-3-031-16852-9_4
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