  Understanding the way cells communicate, co-locate, and interrelate is
essential to understanding human physiology. Hematoxylin and eosin (H&E)
staining is ubiquitously available both for clinical studies and research. The
Colon Nucleus Identification and Classification (CoNIC) Challenge has recently
innovated on robust artificial intelligence labeling of six cell types on H&E
stains of the colon. However, this is a very small fraction of the number of
potential cell classification types. Specifically, the CoNIC Challenge is
unable to classify epithelial subtypes (progenitor, endocrine, goblet),
lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes
(fibroblasts, stromal). In this paper, we propose to use inter-modality
learning to label previously un-labelable cell types on virtual H&E. We
leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify
14 subclasses of cell types. We performed style transfer to synthesize virtual
H&E from MxIF and transferred the higher density labels from MxIF to these
virtual H&E images. We then evaluated the efficacy of learning in this
approach. We identified helper T and progenitor nuclei with positive predictive
values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$
(prevalence $0.07 \pm 0.02$) respectively on virtual H&E. This approach
represents a promising step towards automating annotation in digital pathology.
