Virtual staining for mitosis detection in Breast Histopathology

• URL: http://arxiv.org/abs/2003.07801v1
• Date: Tue, 17 Mar 2020 16:33:34 GMT
• Title: Virtual staining for mitosis detection in Breast Histopathology
• Authors: Caner Mercan, Germonda Reijnen-Mooij, David Tellez Martin, Johannes Lotz, Nick Weiss, Marcel van Gerven, Francesco Ciompi
• Abstract summary: We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures.
• Score: 5.004307299517538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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