Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs

Authors

  • Matthew McDermott MIT
  • Tom Yan MIT
  • Tristan Naumann MIT
  • Nathan Hunt MIT
  • Harini Suresh MIT
  • Peter Szolovits MIT
  • Marzyeh Ghassemi MIT

Keywords:

clinical informatics, biomedical informatics, semi-supervised learning, generative adversarial networks, GANs

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.

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Published

2018-04-26

How to Cite

McDermott, M., Yan, T., Naumann, T., Hunt, N., Suresh, H., Szolovits, P., & Ghassemi, M. (2018). Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11890

Issue

Section

Main Track: Machine Learning Applications