Supervised Statistical Learning for Cancer Detection in Dehydrated Excised Tissue with Terahertz Imaging

Tanny Chavez, Nagma Vohra, Jingxian Wu, Narasimhan Rajaram, Magda El-Shenawee, Keith Bailey

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

Abstract

This paper proposes a new supervised image segmentation algorithm for the detection of breast cancer using terahertz (THz) imaging. Even though unsupervised learning algorithms have achieved promising results in THz image segmentation, reliable segmentation of tissues with three or more regions, such as cancer, fat and muscle, still remains a major challenge. We propose to tackle this challenge by developing a supervised statistical learning method based on multi-class Bayesian ordinal probit regression. The proposed algorithm utilizes a latent variable for the categorical classification of each pixel within the image. The model parameters are estimated through a Markov chain Monte Carlo (MCMC) process during the training phase. Experimental results in murine formalin-fixed paraffin-embedded (FFPE) breast cancer samples demonstrated that the proposed supervised model outperforms alternative unsupervised methods.

Original languageEnglish (US)
Title of host publication2020 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC/URSI 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9-10
Number of pages2
ISBN (Electronic)9781946815088
DOIs
StatePublished - Jul 5 2020
Event2020 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC/URSI 2020 - Toronto, Canada
Duration: Jul 5 2020Jul 10 2020

Publication series

Name2020 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC/URSI 2020 - Proceedings

Conference

Conference2020 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC/URSI 2020
CountryCanada
CityToronto
Period7/5/207/10/20

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Instrumentation

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