Open AccessHyperspectral microscopic imaging for head and neck squamous cell carcinoma detection in histologic images
Author(s) -
Ling Ma,
Ximing Zhou,
James V. Little,
Amy Y. Chen,
Larry L. Myers,
Baran D. Sumer,
Baowei Fei
Publication year - 2021
Publication title -
pubmed central
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
pISSN - 0277-786X
DOI - 10.1117/12.2581970
Subject(s) - hyperspectral imaging , convolutional neural network , head and neck , basal cell , larynx , pathology , head and neck squamous cell carcinoma , medicine , artificial intelligence , deep learning , computer science , radiology , head and neck cancer , anatomy , radiation therapy , surgery
The purpose of this study is to investigate hyperspectral microscopic imaging and deep learning methods for automatic detection of head and neck squamous cell carcinoma (SCC) on histologic slides. Hyperspectral imaging (HSI) cubes were acquired from pathologic slides of 18 patients with SCC of the larynx, hypopharynx, and buccal mucosa. An Inception-based two-dimensional convolutional neural network (CNN) was trained and validated for the HSI data. The automatic deep learning method was tested with independent data of human patients. This study demonstrated the feasibility of using hyperspectral microscopic imaging and deep learning classification to aid pathologists in detecting SCC on histologic slides.