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

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.

Priority queue is one of the fundamental data structures utilized in various areas of informatics such as graph theory, discrete-event simulation, and operating systems. Also, it finds an application in algorithms such as image segmenting used in remote sensing. The literature offers several basic implementations of the priority queue, like the binary heap, which usually offers good complexity to performance tradeoffs. However, the researchers have proposed several advanced implementations, such as the Fibonacci heap or Brodal queue, that offer better theoretical computational complexities. The existing practical comparison suggests that in some use cases, the simpler basic implementations perform better than theoretically better - yet more complicated ones. One of the not yet examined use cases is the OPTICS clustering algorithm. In the paper, we describe how the OPTICS algorithm utilizes the priority queue and we present an experimental comparison of basic and advanced priority queue implementations showing that this use case also favors simpler implementations with worse theoretical computational complexities of their operations.