HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks

Georg Wölflein; In Hwa Um; David J Harrison; Ognjen Arandjelović

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arXiv · 2210.06909

HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks

Georg Wölflein·In Hwa Um·David J Harrison·Ognjen Arandjelović

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Abstract

The presence and density of specific types of immune cells are important to understand a patient's immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma using generative adversarial networks. Our proposed method jointly learns both staining tasks, incentivising the network to incorporate mutually beneficial information from each task. We devise a novel metric to quantify the virtual staining quality, and use it to evaluate our method.

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BibTeX RIS

Georg Wölflein, In Hwa Um, David J Harrison, Ognjen Arandjelović. 2022-10-13. HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks. https://arxiv.org/abs/2210.06909

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