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

Martin Halicek, Maysam Shahedi, James V. Little, Amy Y. Chen, Larry L. Myers, Baran D. Sumer, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
PublisherSPIE
ISBN (Electronic)9781510625594
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Digital Pathology - San Diego, United States
Duration: Feb 20 2019Feb 21 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10956
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Digital Pathology
CountryUnited States
CitySan Diego
Period2/20/192/21/19

Fingerprint

Squamous Cell Carcinoma
Neck
cancer
Head
Neural networks
Area Under Curve
Histology
Research Design
surgeons
histology
Design of experiments
Neoplasms
chutes
Tissue
margins
Pathologists
Carcinoma, squamous cell of head and neck
Epithelial Cells
Testing
Surgeons

Keywords

  • Convolutional neural network
  • Deep learning
  • Digitized whole-slide histology
  • Head and neck cancer
  • Squamous cell carcinoma

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Halicek, M., Shahedi, M., Little, J. V., Chen, A. Y., Myers, L. L., Sumer, B. D., & Fei, B. (2019). Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks. In J. E. Tomaszewski, & A. D. Ward (Eds.), Medical Imaging 2019: Digital Pathology [109560K] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10956). SPIE. https://doi.org/10.1117/12.2512570

Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks. / Halicek, Martin; Shahedi, Maysam; Little, James V.; Chen, Amy Y.; Myers, Larry L.; Sumer, Baran D.; Fei, Baowei.

Medical Imaging 2019: Digital Pathology. ed. / John E. Tomaszewski; Aaron D. Ward. SPIE, 2019. 109560K (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10956).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Halicek, M, Shahedi, M, Little, JV, Chen, AY, Myers, LL, Sumer, BD & Fei, B 2019, Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks. in JE Tomaszewski & AD Ward (eds), Medical Imaging 2019: Digital Pathology., 109560K, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10956, SPIE, Medical Imaging 2019: Digital Pathology, San Diego, United States, 2/20/19. https://doi.org/10.1117/12.2512570
Halicek M, Shahedi M, Little JV, Chen AY, Myers LL, Sumer BD et al. Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks. In Tomaszewski JE, Ward AD, editors, Medical Imaging 2019: Digital Pathology. SPIE. 2019. 109560K. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2512570
Halicek, Martin ; Shahedi, Maysam ; Little, James V. ; Chen, Amy Y. ; Myers, Larry L. ; Sumer, Baran D. ; Fei, Baowei. / Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks. Medical Imaging 2019: Digital Pathology. editor / John E. Tomaszewski ; Aaron D. Ward. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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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.",
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