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Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks

Year: 2019
Type: conference-paper
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.
DOI: 10.1117/12.2512570
Authors: Martin Halicek, Maysam Shahedi, James V. Little, Amy Y. Chen, Larry L. Myers, Baran D. Sumer, Baowei Fei
Institutions: Georgia Institute of Technology, Emory University, The University of Texas at Dallas, Augusta University, The Wallace H. Coulter Department of Biomedical Engineering, The University of Texas Southwestern Medical Center
Language: English
Open Access status: green
OpenAlex ID: W2921839162
FWCI: 0.478
Cites: 21
Cited by: 12
Related to: 10