  A key challenge in cancer immunotherapy biomarker research is quantification
of pattern changes in microscopic whole slide images of tumor biopsies.
Different cell types tend to migrate into various tissue compartments and form
variable distribution patterns. Drug development requires correlative analysis
of various biomarkers in and between the tissue compartments. To enable that,
tissue slides are manually annotated by expert pathologists. Manual annotation
of tissue slides is a labor intensive, tedious and error-prone task. Automation
of this annotation process can improve accuracy and consistency while reducing
workload and cost in a way that will positively influence drug development
efforts. In this paper we present a novel one-shot color deconvolution deep
learning method to automatically segment and annotate digitized slide images
with multiple stainings into compartments of tumor, healthy tissue, and
necrosis. We address the task in the context of drug development where multiple
stains, tissue and tumor types exist and look into solutions for
generalizations over these image populations.
