Editable Stain Transformation Of Histological Images Using Unpaired GANs

Double staining in histopathology is done to help identify tissue features and cell types differentiated between two tissue samples using two different dyes. In the case of metaplastic breast cancer, H&E and P63 are often used in conjunction for diagnosis. However, P63 tends to damage the tissue and is prohibitively expensive, motivating the development of virtual staining methods, or methods of using artificial intelligence in computer vision for diagnostic strain transformation. In this work, we present results of the new xAI-CycleGAN architecture's capability to transform from H&E pathology stain to the P63 pathology stain on samples of breast tissue with presence of metaplastic cancer. The architecture is based on Mask CycleGAN and explainability-enhanced training, and further enhanced by structure-preserving features, and the ability to edit the output to further bring generated samples to ground truth images. We demonstrate its ability to preserve structure well and produce superior quality images, and demonstrate the ability to use output editing to approach real images, and opening the doors for further tuning frameworks to perfect the model using the editing approach. Additionally, we present the results of a survey conducted with histopathologists, evaluating the realism of the generated images through a pairwise comparison task, where we demonstrate the approach produced high quality images that sometimes are indistinguishable from ground truth, and overall our model outputs get a high realism rating.