  Melanoma is one of the most aggressive forms of skin cancer, causing a large
proportion of skin cancer deaths. However, melanoma diagnoses by pathologists
shows low interrater reliability. As melanoma is a cancer of the melanocyte,
there is a clear need to develop a melanocytic cell segmentation tool that is
agnostic to pathologist variability and automates pixel-level annotation.
Gigapixel-level pathologist labeling, however, is impractical. Herein, we
propose a means to train deep neural networks for melanocytic cell segmentation
from hematoxylin and eosin (H&E) stained sections and paired
immunohistochemistry (IHC) of adjacent tissue sections, achieving a mean IOU of
0.64 despite imperfect ground-truth labels.
