Translator Disclaimer
Paper
18 March 2019 Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks
Author Affiliations +
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Martin Halicek, Maysam Shahedi, James V. Little, Amy Y. Chen, Larry L. Myers, Baran D. Sumer, and Baowei Fei "Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560K (18 March 2019); https://doi.org/10.1117/12.2512570
PROCEEDINGS
9 PAGES


SHARE
Advertisement
Advertisement
Back to Top