Abstract
The gold standard for diagnosing cancer is through pathological examination. This typically involves the utilization of staining techniques such as hematoxylin-eosin (H &E) and immunohistochemistry (IHC) as relying solely on H &E can sometimes result in inaccurate cancer diagnoses. IHC examination offers additional evidence to support the diagnostic process. Given challenging accessibility issues of IHC examination, generating virtual IHC images from H &E-stained images presents a viable solution. This study proposes Active Medical Segmentation and Rendering (AMSR), an end-to-end framework for biomarker expression levels prediction and virtual staining, leveraging constrained Generative Adversarial Networks (GAN). The proposed framework mimics the staining processes, surpassing prior works and offering a feasible substitute for traditional histopathology methods. Preliminary results are presented using a clinical trial dataset pertaining to the CEACAM5 biomarker.
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References
Bankhead, P.: Qupath: open source software for digital pathology image analysis. Sci. Rep. 7(1), 1–7 (2017)
Burge, C.N., Chang, H.R., Apple, S.K.: Do the histologic features and results of breast cancer biomarker studies differ between core biopsy and surgical excision specimens? The Breast 15(2), 167–172 (2006)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs (2017)
Conklin, C.M., Craddock, K.J., Have, C., Laskin, J., Couture, C., Ionescu, D.N.: Immunohistochemistry is a reliable screening tool for identification of alk rearrangement in non-small-cell lung carcinoma and is antibody dependent. J. Thorac. Oncol. 8(1), 45–51 (2013)
Decary, S., et al.: A novel anti-ceacam5 maytansinoid-antibody-drug conjugate for the treatment of colorectal, lung and gastric tumors. Cancer Res. 75(15_Supplement), 1688–1688 (2015)
Gatenbee, C.D., et al.: Valis: Virtual alignment of pathology image series. bioRxiv pp. 2021–11 (2021)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Huret, J.L., Ahmad, M., Arsaban, M., Bernheim, A., Cigna, J., Desangles, F., Guignard, J.C., Jacquemot-Perbal, M.C., Labarussias, M., Leberre, V., et al.: Atlas of genetics and cytogenetics in oncology and haematology in 2013. Nucleic Acids Res. 41(D1), D920–D924 (2012)
Karamcheti, S., Krishna, R., Fei-Fei, L., Manning, C.D.: Mind your outliers! investigating the negative impact of outliers on active learning for visual question answering. arXiv preprint arXiv:2107.02331 (2021)
Kuespert, K., Pils, S., Hauck, C.R.: Ceacams: their role in physiology and pathophysiology. Curr. Opin. Cell Biol. 18(5), 565–571 (2006)
Lin, X., Parikh, D.: Active learning for visual question answering: An empirical study. arXiv preprint arXiv:1711.01732 (2017)
Liu, S., Zhang, B., Liu, Y., Han, A., Shi, H., Guan, T., He, Y.: Unpaired stain transfer using pathology-consistent constrained generative adversarial networks. IEEE Trans. Med. Imaging 40(8), 1977–1989 (2021)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation (2015)
Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomed. Engi. 5(6), 555–570 (2021)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Park, T., Efros, A.A., Zhang, R., Zhu, J.Y.: Contrastive learning for unpaired image-to-image translation. In: Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX 16, pp. 319–345. Springer (2020)
Simon, R.: Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Pers. Med. 7(1), 33–47 (2010)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Zhang, X., Han, X., Zuo, P., Zhang, X., Xu, H.: Ceacam5 stimulates the progression of non-small-cell lung cancer by promoting cell proliferation and migration. J. Int. Med. Res. 48(9), 0300060520959478 (2020)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
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Zhao, W. et al. (2024). Clinical Trial Histology Image Based End-to-End Biomarker Expression Levels Prediction and Visualization Using Constrained GANs. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2023. Lecture Notes in Computer Science, vol 14313. Springer, Cham. https://doi.org/10.1007/978-3-031-47076-9_1
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