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

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

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

Abstract

The study is to incorporate polarized hyperspectral imaging (PHSI) with deep learning for automatic detection of head and neck squamous cell carcinoma (SCC) on hematoxylin and eosin (H&E) stained tissue slides. A polarized hyperspectral imaging microscope had been developed in our group. In this paper, we firstly collected the Stokes vector data cubes (S0, S1, S2, and S3) of histologic slides from 17 patients with SCC by the PHSI microscope, under the wavelength range from 467 nm to 750 nm. Secondly, we generated the synthetic RGB images from the original Stokes vector data cubes. Thirdly, we cropped the synthetic RGB images into image patches at the image size of 96x96 pixels, and then set up a ResNet50- based convolutional neural network (CNN) to classify the image patches of the four Stokes vector parameters (S0, S1, S2, and S3) by application of transfer learning. To test the performances of the model, each time we trained the model based on the image patches (S0, S1, S2, and S3) of 16 patients out of 17 patients, and used the trained model to calculate the testing accuracy based on the image patches of the rest 1 patient (S0, S1, S2, and S3). We repeated the process for 6 times and obtained 24 testing accuracies (S0, S1, S2, and S3) from 6 different patients out of the 17 patients. The preliminary results showed that the average testing accuracy (84.2%) on S3 outperformed the average testing accuracy (83.5%) on S0. Furthermore, 4 of 6 testing accuracies of S3 (96.0%, 87.3%, 82.8%, and 86.7%) outperformed the testing accuracies of S0 (93.3%, 85.2%, 80.2%, and 79.0%). The study demonstrated the potential of using polarized hyperspectral imaging and deep learning for automatic detection of head and neck SCC on pathologic slides.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationDigital and Computational Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward, Richard M. Levenson
PublisherSPIE
ISBN (Electronic)9781510649538
DOIs
StatePublished - 2022
EventMedical Imaging 2022: Digital and Computational Pathology - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12039
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2022: Digital and Computational Pathology
CityVirtual, Online
Period3/21/223/27/22

Keywords

  • deep learning
  • digital pathology
  • head and neck cancer
  • histologic slides
  • Polarized hyperspectral imaging
  • Stokes vector

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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