  Understanding the way cells communicate, co-locate, and interrelate is
essential to furthering our understanding of how the body functions. H&E is
widely available, however, cell subtyping often requires expert knowledge and
the use of specialized stains. To reduce the annotation burden, AI has been
proposed for the classification of cells on H&E. For example, the recent Colon
Nucleus Identification and Classification (CoNIC) Challenge focused on labeling
6 cell types on H&E of the colon. However, the CoNIC Challenge was unable to
classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte
subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We
use inter-modality learning to label previously un-labelable cell types on H&E.
We take advantage of multiplexed immunofluorescence (MxIF) histology to label
14 cell subclasses. We performed style transfer on the same MxIF tissues to
synthesize realistic virtual H&E which we paired with the MxIF-derived cell
subclassification labels. We evaluated the efficacy of using a supervised
learning scheme where the input was realistic-quality virtual H&E and the
labels were MxIF-derived cell subclasses. We assessed our model on private
virtual H&E and public real H&E. On virtual H&E, we were able to classify
helper T cells and epithelial progenitors 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, when using ground truth centroid information. On
real H&E we could classify helper T cells and epithelial progenitors with upper
bound positive predictive values of $0.43 \pm 0.03$ (parent class prevalence
0.21) and $0.94 \pm 0.02$ (parent class prevalence 0.49) when using ground
truth centroid information. This is the first work to provide cell type
classification for helper T and epithelial progenitor nuclei on H&E.
