We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from HE 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.
|Title of host publication||2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)|
|Number of pages||5|
|Publication status||Published - 04.2020|
|Event||17th IEEE International Symposium on Biomedical Imaging |
- Iowa City, United States
Duration: 03.04.2020 → 07.04.2020
Conference number: 160183