Open-set recognition of breast cancer treatments

Alexander Cao*, Diego Klabjan, Yuan Luo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or “unknown.” As novel cancer drug cocktails with improved treatment are continually discovered, classifying patients by treatments can naturally be formulated in terms of an open-set recognition problem. Drawbacks, due to modeling unknown samples during training, arise from straightforward implementations of prior work in healthcare open-set learning. Accordingly, we reframe the problem methodology and apply a recent Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data. Not only do we obtain more accurate and robust classification results (14% average F1 increase compared to recent methods), but we also reexamine open-set recognition in terms of deployability to a clinical setting.

Original languageEnglish (US)
Article number102451
JournalArtificial Intelligence In Medicine
StatePublished - Jan 2023


  • Autoencoder
  • Breast cancer treatments
  • Deep neural networks
  • Open-set recognition

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence


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