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Learning Melanocytic Cell Masks from Adjacent Stained Tissue

2022-11-01 00:40:09
Mikio Tada, Maria L. Wei, Michael J. Keiser
arXiv_AI
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2211.00646

PDF

https://arxiv.org/pdf/2211.00646.pdf

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