  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.
