Automatic inference of BI-RADS final assessment categories from narrative mammography report findings

Imon Banerjee, Selen Bozkurt, Emel Alkim, Hersh Sagreiya, Allison W. Kurian, Daniel L. Rubin

Research output: Contribution to journalArticlepeer-review


We propose an efficient natural language processing approach for inferring the BI-RADS final assessment categories by analyzing only the mammogram findings reported by the mammographer in narrative form. The proposed hybrid method integrates semantic term embedding with distributional semantics, producing a context-aware vector representation of unstructured mammography reports. A large corpus of unannotated mammography reports (300,000) was used to learn the context of the key-terms using a distributional semantics approach, and the trained model was applied to generate context-aware vector representations of the reports annotated with BI-RADS category (22,091). The vectorized reports were utilized to train a supervised classifier to derive the BI-RADS assessment class. Even though the majority of the proposed embedding pipeline is unsupervised, the classifier was able to recognize substantial semantic information for deriving the BI-RADS categorization not only on a holdout internal testset and also on an external validation set (1900 reports). Our proposed method outperforms a recently published domain-specific rule-based system and could be relevant for evaluating concordance between radiologists. With minimal requirement for task specific customization, the proposed method can be easily transferable to a different domain to support large scale text mining or derivation of patient phenotype.

Original languageEnglish (US)
Article number103137
JournalJournal of Biomedical Informatics
StatePublished - Apr 2019


  • BI-RADS classification
  • Deep learning
  • Distributional semantics
  • Mammography report
  • NLP
  • Text mining

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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