Virtual staining for mitosis detection in breast histopathology
Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE)
InProceedings IEEE ISBI 2020: International Symposium on Biomedical Imaging, pp. 1770-1774
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (Iowa City, Iowa, USA, April 3-7, 2020)
Article in monograph or in proceedings
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SW OZ DCC AI
Proceedings IEEE ISBI 2020: International Symposium on Biomedical Imaging
SubjectCognitive artificial intelligence
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine breast cancer diagnosis and grading. We propose several scenarios, in which CNN trained with synthetically generated histopathology images perform on par with or even better than the same baseline model trained with real images. We discuss the potential of this application to scale the number of training samples without the need for manual annotations.
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