Modelling the Ki67 Index in Synthetic HE-Stained Images Using Conditional StyleGAN Model

Hematoxylin and Eosin (HE) staining is gold standard in histopathological examination of cancer tissue, representing first step towards cancer diagnosis. Second step is series of immunohistochemical stainings, including cell proliferation marker called Ki67 index. Deep learning models offer promising solutions for improving medical diagnostics, while generative models provide additional explainability of predictive models, which is essential for their adoption in clinical practice. Our previous work introduced novel approach that utilises conditional StyleGAN model for generating HE-stained images conditioned on Ki67 index. This study proposes to employ this model for generating sequences of HE-stained images reflecting varying Ki67 index values. Sequences enable exploration of hidden relationships between HE and Ki67 staining and can enhance explainability of predictive models, e.g., by generating counterfactual examples. While our previous research focused on assessing quality of generated HE images, this study extends that work by evaluating model’s ability to capture Ki67-related variations in HE-stained images. Additionally, expert pathologists evaluated generated sequences, proposing criteria for assessing their relevance. Our findings demonstrate potential of conditional StyleGAN model as part of explainable framework for analysing and predicting immunohistochemical information from HE-stained images. Results highlight relevance of generative models in histopathology and their potential applications in cancer progression analysis.