Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs


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


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


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.




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



Main Track: Machine Learning Applications