Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and machine learning

Ximing Zhou, Ling Ma, William Brown, James V. Little, Amy Y. Chen, Larry L. Myers, Baran D. Sumer, Baowei Fei

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

17 Scopus citations


The aim of this study is to incorporate polarized hyperspectral imaging (PHSI) with machine learning for automatic detection of head and neck cancer on H&E stained tissue slides. A polarized hyperspectral imaging microscope had been developed in our group. The preliminary results showed that the spectral curves of the Stokes vector parameters (S0, S1, S2, S3) on the H&E stained tissue slides with squamous cell carcinoma (SCC) from the larynx are different from normal tissue in certain wavelength range. In this paper, we imaged 20 H&E stained tissue slides from 10 patients with SCC of the larynx by the PHSI microscope. Several machine learning algorithms (support vector machine, random forest, Gaussian naive Bayes, logistic regression) were applied to the collected image data for the automatic detection of SCC on the H&E stained tissue slides. The performance of these methods was compared among the collected polarized hyperspectral imaging dataset of tissue slides, the pseudo RGB images generated from the polarized hyperspectral imaging dataset, and the principle component analysis (PCA) transformation of the polarized hyperspectral imaging dataset. The results suggest that SVM is a superior classifier for the classification task based on polarized hyperspectral data cubes compared to the other three types of classifiers. Furthermore, the incorporation of the four Stokes vector parameters improved the classification accuracy. Finally, the PCA transformed image data did not improve the accuracy as it might lose some important information from the original polarized hyperspectral data cube. Polarized hyperspectral imaging can have many potential applications in digital pathology.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
ISBN (Electronic)9781510640351
StatePublished - 2021
EventMedical Imaging 2021: Digital Pathology - Virtual, Online, United States
Duration: Feb 15 2021Feb 19 2021

Publication series

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


ConferenceMedical Imaging 2021: Digital Pathology
Country/TerritoryUnited States
CityVirtual, Online

ASJC Scopus subject areas

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


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