FUSION: Fully Unsupervised Test-Time Stain Adaptation via Fused Normalization Statistics

1. 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)

   Article  Google Scholar 

2. Ganin, Y., Lempitsky, V.S.: Unsupervised domain adaptation by backpropagation. arXiv abs/1409.7495 (2015)

   Google Scholar 

3. Gehlot, S., Gupta, A.: Self-supervision based dual-transformation learning for stain normalization, classification and segmentation. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds.) MLMI 2021. LNCS, vol. 12966, pp. 477–486. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87589-3_49

   Chapter  Google Scholar 

4. Gupta, A., et al.: GCTI-SN: geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images. Med. Image Anal. 65, 101788 (2020)

   Article  Google Scholar 

5. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015)

   Google Scholar 

6. 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)

   Google Scholar 

7. Lin, T., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. CoRR abs/1612.03144 (2016)

   Google Scholar 

8. Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: ISBI, pp. 1107–1110 (2009)

   Google Scholar 

9. Magee, D., et al.: Colour normalisation in digital histopathology images. In: Proceedings of the Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy (MICCAI Workshop), vol. 100 (2009)

   Google Scholar 

10. 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)

    Google Scholar 

11. Nado, Z., Padhy, S., Sculley, D., D’Amour, A., Lakshminarayanan, B., Snoek, J.: Evaluating prediction-time batch normalization for robustness under covariate shift. CoRR abs/2006.10963 (2020). https://arxiv.org/abs/2006.10963

12. Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 5, 34–41 (2001)

    Article  Google Scholar 

13. 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)

    Article  Google Scholar 

14. 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)

    Google Scholar 

15. Schneider, S., Rusak, E., Eck, L., Bringmann, O., Brendel, W., Bethge, M.: Improving robustness against common corruptions by covariate shift adaptation. CoRR abs/2006.16971 (2020). https://arxiv.org/abs/2006.16971

16. Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: StainGAN: stain style transfer for digital histological images. arXiv preprint arXiv:1804.01601 (2018)

17. Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A.A., Hardt, M.: Test-time training for out-of-distribution generalization. CoRR abs/1909.13231 (2019)

    Google Scholar 

18. Abe, T., Murakami, Y., Yamaguchi, M.: Color correction of pathological images based on dye amount quantification. Opt. Rev. 12(4), 293–300 (2005)

    Article  Google Scholar 

19. Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. CoRR abs/1905.11946 (2019)

    Google Scholar 

20. 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)

    Article  Google Scholar 

21. Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. CoRR abs/1510.02192 (2015)

    Google Scholar 

22. Vahadane, A., et al.: Structure-preserved color normalization for histological images. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 1012–1015 (2015)

    Google Scholar 

23. Veta, M., et al.: Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Med. Image Anal. 54, 111–121 (2019)

    Article  Google Scholar 

24. Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: fully test-time adaptation by entropy minimization. In: International Conference on Learning Representations (2021). https://openreview.net/forum?id=uXl3bZLkr3c

25. 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)

    Google Scholar 

26. 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 (2018). https://doi.org/10.1109/ISBI.2018.8363641

27. Zhang, M., Levine, S., Finn, C.: MEMO: test time robustness via adaptation and augmentation. CoRR abs/2110.09506 (2021). https://arxiv.org/abs/2110.09506

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