Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning

Minh Ha Tran, Ling Ma, James V. Litter, Amy Y. Chen, Baowei Fei

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

1 Scopus citations


Hyperspectral imaging (HSI), a non-invasive imaging modality, has been successfully used in many different biological and medical applications. One such application is in the field of oncology, where hyperspectral imaging is being used on histologic samples. This study compares the performances of different image classifiers using different imaging modalities as training data. From a database of 33 fixed tissues from head and neck patients with follicular thyroid carcinoma, we produced three different datasets: an RGB image dataset that was acquired from a whole slide image scanner, a hyperspectral (HS) dataset that was acquired with a compact hyperspectral camera, and an HS-synthesized RGB image dataset. Three separate deep learning classifiers were trained using the three datasets. We show that the deep learning classifier trained on HSI data has an area under the receiver operator characteristic curve (AUC-ROC) of 0.966, higher than that of the classifiers trained on RGB and HSI-synthesized RGB data. This study demonstrates that hyperspectral images improve the performance of cancer classification on whole histologic slides. Hyperspectral imaging and deep learning provide an automatic tool for thyroid cancer detection on whole histologic 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
ISBN (Electronic)9781510649538
StatePublished - 2022
Externally publishedYes
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
ISSN (Print)1605-7422


ConferenceMedical Imaging 2022: Digital and Computational Pathology
CityVirtual, Online


  • deep learning
  • digital pathology
  • Hyperspectral imaging
  • thyroid cancer
  • whole-slide histologic imaging

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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