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.
@article{arxiv.2211.00646,
title = {Learning Melanocytic Cell Masks from Adjacent Stained Tissue},
author = {Mikio Tada and Ursula E. Lang and Iwei Yeh and Elizabeth S. Keiser and Maria L. Wei and Michael J. Keiser},
journal= {arXiv preprint arXiv:2211.00646},
year = {2024}
}
Comments
Accepted at Medical Image Learning with Limited & Noisy Data Workshop, Medical Image Computing and Computer Assisted Interventions (MICCAI) 2022