Semi-supervised biomedical translation with cycle Wasserstein regression GaNs
Name
semi-supervised-CWR-GAN_Ghassemi.pdf
Description
Accepted version
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568.1 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
McDermott, Matthew
Yan, Tom
Naumann, Tristan
Hunt, Nathan
Suresh, Harini S.
Szolovits, Peter
Ghassemi, Marzyeh
Date Issued
2018
Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Citation
McDermott, Matthew B. A. et al. "Semi-Supervised Biomedical Translation with Cycle Wasserstein Regression GANs." AAAI Conference on Artificial Intelligence, February 2018, New Orleans, AAAI, 2018.
Version
Author's final manuscript
Abstract
The biomedical field offers many learning tasks that share unique challenges: large amounts of unpaired data, and a high cost to generate labels. In this work, we develop a method to address these issues with semi-supervised learning in regression tasks (e.g., translation from source to target). Our model uses adversarial signals to learn from unpaired datapoints, and imposes a cycle-loss reconstruction error penalty to regularize mappings in either direction against one another. We first evaluate our method on synthetic experiments, demonstrating two primary advantages of the system: 1) distribution matching via the adversarial loss and 2) regularization towards invertible mappings via the cycle loss. We then show a regularization effect and improved performance when paired data is supplemented by additional unpaired data on two real biomedical regression tasks: estimating the physiological effect of medical treatments, and extrapolating gene expression (transcriptomics) signals. Our proposed technique is a promising initial step towards more robust use of adversarial signals in semi-supervised regression, and could be useful for other tasks (e.g., causal inference or modality translation) in the biomedical field.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16938/15951
