Deep integrative analysis for survival prediction

Chenglong Huang, Albert Zhang, Guanghua Xiao

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

5 Scopus citations


Survival prediction is very important in medical treatment. However, recent leading research is challenged by two factors: 1) the datasets usually come with multi-modality; and 2) sample sizes are relatively small. To solve the above challenges, we developed a deep survival learning model to predict patients’ survival outcomes by integrating multi-view data. The proposed network contains two sub-networks, one view-specific and one common sub-network. We designated one CNN-based and one FCN-based sub-network to efficiently handle pathological images and molecular profiles, respectively. Our model first explicitly maximizes the correlation among the views and then transfers feature hierarchies from view commonality and specifically fine-tunes on the survival prediction task. We evaluate our method on real lung and brain tumor data sets to demonstrate the effectiveness of the proposed model using data with multiple modalities across different tumor types.

Original languageEnglish (US)
Title of host publicationPACIFIC SYMPOSIUM ON BIOCOMPUTING 2018
PublisherWorld Scientific Publishing Co. Pte Ltd
Number of pages10
ISBN (Print)9789813235533
StatePublished - Jan 1 2018
Event23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States
Duration: Jan 3 2018Jan 7 2018


Other23rd Pacific Symposium on Biocomputing, PSB 2018
Country/TerritoryUnited States
CityKohala Coast


  • Deep learning
  • Integrative analysis
  • Survival prediction

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics


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