@inproceedings{2472b9cb084f47e498b779d68e5394a9,
title = "Cancer cell segmentation for cellularity prediction via a weakly labeled/strongly labeled hybrid convolutional neural network",
abstract = "In our work, we present an approach to regressing breast cancer cellularity in patches extracted from Whole Slide Imagery (WSI) on Hematoxylin and Eosin (H&E) stains using a fully-convolutional neural network which is trained with two heads: one which computes a global average pool for weakly-labeled data (data with a cellularity score of 0- 1.0) and another which enforces pixel-wise activations for strongly-labeled (segmentation) data. Our method was the top-performing algorithm of all submissions to the BreastPathQ challenge, achieving a prediction probability of 0.941.",
keywords = "Digital pathology, cancer cellularity, convolutional neural networks, whole slide imagery",
author = "Chambers, {David R.} and Brimhall, {Bradley B.} and Poole, {Donald R.} and Medina, {Edward A.}",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE.; Medical Imaging 2022: Digital and Computational Pathology ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2611636",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Tomaszewski, {John E.} and Ward, {Aaron D.} and Levenson, {Richard M.}",
booktitle = "Medical Imaging 2022",
}