| Literature DB >> 32476700 |
Martin Halicek1,2,3, Maysam Shahedi1, James V Little4, Amy Y Chen5, Larry L Myers6, Baran D Sumer6, Baowei Fei1,7,8.
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.Entities:
Keywords: Head and neck cancer; convolutional neural network; deep learning; digitized whole-slide histology; squamous cell carcinoma
Year: 2019 PMID: 32476700 PMCID: PMC7261614 DOI: 10.1117/12.2512570
Source DB: PubMed Journal: Proc SPIE Int Soc Opt Eng ISSN: 0277-786X