A dual stream network for tumor detection in hyperspectral images

P. J.C. Weijtmans, C. Shan, T. Tan, S. G. Brouwer De Koning, T. J.M. Ruers

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)

Abstract

Hyperspectral imaging has become an emerging imaging modality for medical applications. In this work, we propose to combine both the spectral and structural information in the hyperspectral data cube for tumor detection in tongue tissue. A dual stream network is designed, with a spectral and a structural branch. Hyperspectral data (480 to 920 nm) is collected from 7 patients with tongue squamous cell carcinoma. Histopathological analysis provided ground truth labels. The proposed dual stream model outperforms the pure spectral and structural approaches with areas under the ROC-curve of 0.90, 0.87 and 0.85, respectively.

Original languageEnglish
Title of host publicationIEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
PublisherIEEE Computer Society
Pages1256-1259
Number of pages4
ISBN (Electronic)9781538636411
DOIs
Publication statusPublished - 1 Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019
Conference number: 16
https://biomedicalimaging.org/2019/

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Abbreviated titleISBI 2019
CountryItaly
CityVenice
Period8/04/1911/04/19
Internet address

Keywords

  • Hyperspectral imaging
  • Machine learning
  • Neural networks
  • Tongue tumor

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