Veritas: Combining expert opinions without labeled data

Sharath R. Cholleti, Sally A. Goldman, Avrim Blum, David G. Politte, Steven Don, Kirk Smith, Fred Prior

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


We consider a variation of the problem of combining expert opinions for the situation in which there is no ground truth to use for training. Even though we do not have labeled data, the goal of this work is quite different from an unsupervised learning problem in which the goal is to cluster the data. Our work is motivated by the application of segmenting a lung nodule in a computed tomography (CT) scan of the human chest. The lack of a gold standard of truth is a critical problem in medical imaging. A variety of experts, both human and computer algorithms, are available that can mark which voxels are part of a nodule. The question is, how to combine these expert opinions to estimate the unknown ground truth. We present the Veritas algorithm that predicts the underlying label using the knowledge in the expert opinions even without the benefit of any labeled data for training. We evaluate Veritas using artificial data and real CT images to which synthetic nodules have been added, providing a known ground truth.

Original languageEnglish
Pages (from-to)633-651
Number of pages19
JournalInternational Journal on Artificial Intelligence Tools
Issue number5
StatePublished - Oct 2009


  • Boosting
  • Combining experts
  • Interobserver variability
  • Machine learning
  • Medical images


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