Dempster-Shafer theory based feature selection with sparse constraint for outcome prediction in cancer therapy

Chunfeng Lian, Su Ruan, Thierry Denœux, Hua Li, Pierre Vera

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

Abstract

As a pivotal task in cancer therapy, outcome prediction is the foundation for tailoring and adapting a treatment planning. In this paper, we propose to use image features extracted from PET and clinical characteristics. Considering that both information sources are imprecise or noisy, a novel prediction model based on Dempster-Shafer theory is developed. Firstly, a specific loss function with sparse regularization is designed for learning an adaptive dissimilarity metric between feature vectors of labeled patients. Through minimizing this loss function, a linear low-dimensional transformation of the input features is then achieved; meanwhile, thanks to the sparse penalty, the influence of imprecise input features can also be reduced via feature selection. Finally, the learnt dissimilarity metric is used with the Evidential K-Nearest-Neighbor (EK-NN) classifier to predict the outcome.We evaluated the proposed method on two clinical data sets concerning to lung and esophageal tumors, showing good performance.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings
EditorsAlejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells
PublisherSpringer
Pages695-702
Number of pages8
ISBN (Print)9783319245737
DOIs
StatePublished - 2015
Externally publishedYes
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9351
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Country/TerritoryGermany
CityMunich
Period10/5/1510/9/15

Keywords

  • Dempster-Shafer theory
  • Feature selection
  • Outcome prediction
  • PET
  • Sparse constraint

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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