Virtual staining for mitosis detection in Breast Histopathology
This approach could scale training samples for mitosis detection in breast cancer diagnosis without manual annotations, though it is incremental as it builds on existing GAN and CNN methods.
The paper tackles the problem of detecting mitotic figures in breast cancer histopathology by using GANs to virtually stain images between H&E and PHH3, enabling CNN models trained on synthetic images to perform on par or better than those trained on real images.
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.