@inproceedings{48aaaa235ee24132a5878290dafb11ef,
title = "Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks",
abstract = "Primary management for head and neck squamous cell carcinoma (SCC) involves surgical resection with negative cancer margins. Pathologists guide surgeons during these operations by detecting SCC in histology slides made from the excised tissue. In this study, 192 digitized histological images from 84 head and neck SCC patients were used to train, validate, and test an inception-v4 convolutional neural network. The proposed method performs with an AUC of 0.91 and 0.92 for the validation and testing group. The careful experimental design yields a robust method with potential to help create a tool to increase efficiency and accuracy of pathologists for detecting SCC in histological images.",
keywords = "Convolutional neural network, Deep learning, Digitized whole-slide histology, Head and neck cancer, Squamous cell carcinoma",
author = "Martin Halicek and Maysam Shahedi and Little, {James V.} and Chen, {Amy Y.} and Myers, {Larry L.} and Sumer, {Baran D.} and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2019 SPIE.; Medical Imaging 2019: Digital Pathology ; Conference date: 20-02-2019 Through 21-02-2019",
year = "2019",
doi = "10.1117/12.2512570",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Tomaszewski, {John E.} and Ward, {Aaron D.}",
booktitle = "Medical Imaging 2019",
}