Abstract
Progesterone receptor (PR) plays a vital role in diagnosing and treating breast cancer, but PR staining is costly and time-consuming, seriously hindering its application in clinical practice. The recent rapid development of deep learning technology provides an opportunity to address this problem by virtual staining. However, supervised methods acquire pixel-level paired H &E and PR images, which almost cannot be implemented clinically. In addition, unsupervised methods lack effective constraint information, and the staining results are not reliable sometimes. In this paper, we propose a semi-supervised PR virtual staining method without any pathologist annotation. Firstly, we register the consecutive slides and obtain the patch-level labels of H &E images from the registered consecutive PR images. Furthermore, by designing a Pos/Neg classifier and corresponding constraints, the output images maintain the Pos/Neg consistency with the input images, enabling the output images to be more accurate. Experimental results show that our method can effectively generate PR images from H &E images and maintain structural and pathological consistency with the reference. Compared with existing methods, our approach achieves the best performance.
B. Zeng, Y. Lin and Y. Wang—Co-first authors.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (61922048 &62031023), in part by the Shenzhen Science and Technology Project (JCYJ20200109142808034), and in part by Guangdong Special Support (2019TX05X187).
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Zeng, B. et al. (2022). Semi-supervised PR Virtual Staining for Breast Histopathological Images. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_23
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