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arXiv · 2312.03647

Editable Stain Transformation Of Histological Images Using Unpaired GANs

Tibor Sloboda·Lukáš Hudec·Wanda Benešová
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Abstract

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.

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Tibor Sloboda, Lukáš Hudec, Wanda Benešová. 2023-12-06. Editable Stain Transformation Of Histological Images Using Unpaired GANs. https://arxiv.org/abs/2312.03647

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