Abstract
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 language | English (US) |
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Title of host publication | PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018 |
Publisher | World Scientific Publishing Co. Pte Ltd |
Pages | 343-352 |
Number of pages | 10 |
Edition | 212669 |
ISBN (Print) | 9789813235533 |
DOIs | |
State | Published - Jan 1 2018 |
Event | 23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States Duration: Jan 3 2018 → Jan 7 2018 |
Other
Other | 23rd Pacific Symposium on Biocomputing, PSB 2018 |
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Country/Territory | United States |
City | Kohala Coast |
Period | 1/3/18 → 1/7/18 |
Keywords
- Deep learning
- Integrative analysis
- Survival prediction
ASJC Scopus subject areas
- Biomedical Engineering
- Computational Theory and Mathematics