Martin Halicek,1,2,3 Maysam Shahedi,1 James V. Little,4 Amy Y. Chen,4 Larry L. Myers,5 Baran D. Sumer,6 Baowei Feihttps://orcid.org/0000-0002-9123-94841,7
1The Univ. of Texas at Dallas (United States) 2Georgia Institute of Technology & Emory Univ. School of Medicine (United States) 3Medical College of Georgia, Augusta Univ. (United States) 4Emory Univ. School of Medicine (United States) 5The Univ. of Texas Southwestern Medical Ctr. (United States) 6The Univ. of Texas Southwestern Medical Ctr. (United States) 7Univ. of Texas Southwestern Medical Ctr. (United States)
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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.
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Martin Halicek, Maysam Shahedi, James V. Little, Amy Y. Chen, Larry L. Myers, Baran D. Sumer, 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