Abstract
Stain color variation s across images are common in the medical imaging domain. However, such variations among the training and test datasets may lead to unsatisfactory performance on the latter in any desired task. This paper proposes a novel coupled-network composed of two U-Net type architectures that utilize self-supervised learning. The first subnetwork (N1) learns an identity transformation, while the second (N2) learns a transformation to perform stain normalization. We also introduce classification heads in the subnetworks, trained along with the stain normalization task. To the best of our knowledge, the proposed coupling framework, where the information from the encoders of both the subnetworks is utilized by the decoders of both subnetworks as well as trained in a coupled fashion, is introduced in this domain for the first time. Interestingly, the coupling of N1 (for identity transformation) and N2 (for stain normalization) helps N2 learn the stain normalization task while being cognizant of the features essential to reconstruct images. Similarly, N1 learns to extract relevant features for reconstruction invariant to stain color variations due to its coupling with N2. Thus, the two subnetworks help each other, leading to improved performance on the subsequent task of classification. Further, it is shown that the proposed architecture can also be used for segmentation, making it applicable for all three applications: stain normalization, classification, and segmentation. Experiments are carried out on four datasets to show the efficacy of the proposed architecture.
Shiv Gehlot would like to thank University Grant Commission (UGC), Govt. of India for the UGC-Senior Research Fellowship. We also acknowledge the Infosys Center for Artificial Intelligence, IIIT-Delhi for our research work.
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References
data science bowl. https://www.kaggle.com/c/data-science-bowl-2018. Accessed 5 Feb 2021
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 43, 99–111 (2015)
Bándi, 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 (2019)
Gupta, A., et al.: GCTI-SN: geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images. Med. Image Anal. 65, 101788 (2020)
Haghighi, F., Hosseinzadeh Taher, M.R., Zhou, Z., Gotway, M.B., Liang, J.: Learning semantics-enriched representation via self-discovery, self-classification, and self-restoration. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 137–147. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_14
Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall, Inc. (1989)
Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: a survey (2019)
Kothari, S., et al.: Automatic batch-invariant color segmentation of histological cancer images. In: From Nano to Macro, 2011 IEEE International Symposium on Biomedical Imaging, pp. 657–660 (2011)
Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: ISBI, pp. 1107–1110 (2009)
Magee, D., et al.: Colour normalisation in digital histopathology images. In: Proceedings Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy (MICCAI Workshop), vol. 100 (2009)
McCann, M.T., Majumdar, J., Peng, C., Castro, C.A., Kovačević, J.: Algorithm and benchmark dataset for stain separation in histology images. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 3953–3957 (2014)
Mehta, S., Mercan, E., Bartlett, J., Weaver, D., Elmore, J.G., Shapiro, L.: Y-net: joint segmentation and classification for diagnosis of breast biopsy images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 893–901. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_99
Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graphics Appl. 5, 34–41 (2001)
Ruderman, D.L., Cronin, T.W., Chiao, C.C.: Statistics of cone responses to natural images: implications for visual coding. JOSA A 15(8), 2036–2045 (1998)
Ruifrok, A., Ruifrok, D.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. /Int. Acad. Cytol. [and] Am. Soc. Cytol. 23(4), 291–299 (2001)
Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: StainGAN: stain style transfer for digital histological images. arXiv preprint arXiv:1804.01601 (2018)
Abe, T., Murakami, Y., Yamaguchi, M.: Color correction of pathological images based on dye amount quantification. Opt. Rev. 12(4), 293–300 (2005)
Tabesh, A., et al.: Multifeature prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans. Med. Imaging 26(10), 1366–1378 (2007)
Tellez, D., et al.: Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med. Image Anal. 58, 101544 (2019)
Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant CNNs for digital pathology (2018)
Zanjani, F.G., Zinger, S., Bejnordi, B.E., van der Laak, J.A.W.M.: Histopathology stain-color normalization using deep generative models. In: 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), pp. 1–11 (2018)
Zanjani, F.G., Zinger, S., Bejnordi, B.E., van der Laak, J.A.W.M., de With, P.H.N.: Stain normalization of histopathology images using generative adversarial networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 573–577, April 2018.https://doi.org/10.1109/ISBI.2018.8363641
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Gehlot, S., Gupta, A. (2021). Self-supervision Based Dual-Transformation Learning for Stain Normalization, Classification andSegmentation. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_49
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