Editable Stain Transformation Of Histological Images Using Unpaired GANs
Tibor Sloboda Lukáš Hudec Wanda Benešová
Double staining in histopathology, particularly for metaplastic breast
cancer, typically employs H&E and P63 dyes. However, P63's tissue damage and
high cost necessitate alternative methods. This study introduces xAI-CycleGAN,
an advanced architecture combining Mask CycleGAN with explainability features
and structure-preserving capabilities for transforming H&E stained breast
tissue images into P63-like images. The architecture allows for output editing,
enhancing resemblance to actual images and enabling further model refinement.
We showcase xAI-CycleGAN's efficacy in maintaining structural integrity and
generating high-quality images. Additionally, a histopathologist survey
indicates the generated images' realism is often comparable to actual images,
validating our model's high-quality output.