Deep Disentangled Representation Learning of Pet Images for Lymphoma Outcome Prediction

Yu Guo, Pierre Decazes, Stephanie Becker, Hua Li, Su Ruan

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

Abstract

Image feature extraction based on deep disentangled representation learning of PET images is proposed for the prediction of lymphoma treatment response. Our method encodes PET images as spatial representations and modality representations by performing supervised tumor segmentation and image reconstruction. In this way, the whole image features (global features) as well as tumor region features (local features) can be extracted without the labor-intensive tumor segmentation and feature calculation procedure. The learned global and local image features are then joined with several prognostic factors evaluated by physicians based on clinical information, and used as input of a SVM classifier for predicting outcome results of lymphoma patients. In this study, 186 lymphoma patient data were included for training and testing the proposed model. The proposed method was compared with the traditional straightforward feature extraction method. The better prediction results of the proposed method also show its efficiency for prognostic prediction related feature extraction in PET images.

Original languageEnglish (US)
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages2018-2021
Number of pages4
ISBN (Electronic)9781538693308
DOIs
StatePublished - Apr 2020
Externally publishedYes
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: Apr 3 2020Apr 7 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2020-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Country/TerritoryUnited States
CityIowa City
Period4/3/204/7/20

Keywords

  • lymphoma
  • modality representation
  • PET images
  • spatial representation
  • treatment response prediction

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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