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 slides using paired
immunohistochemical (IHC) slides of adjacent tissue sections, achieving a mean
IOU of 0.64 despite imperfect ground-truth labels.Comment: {Medical Image Learning with Limited & Noisy Data Workshop at MICCAI
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