Multi-view probabilistic classification of breast microcalcifications

Alan Joseph Bekker, Moran Shalhon, Hayit Greenspan, Jacob Goldberger*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. In this paper we apply a multi-view-classifier for the task. We describe a two-step classification method that is based on a view-level decision, implemented by a logistic regression classifier, followed by a stochastic combination of the two view-level indications into a single benign or malignant decision. The proposed method was evaluated on a large number of cases from a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the advantage of the proposed multi-view classification algorithm that automatically learns the best way to combine the views.

Original languageEnglish
Article number2488019
Pages (from-to)645-6536
Number of pages5892
JournalIEEE Transactions on Medical Imaging
Issue number2
StatePublished - 1 Feb 2016


  • Computer-aided diagnosis (CADx)
  • Curvelet transform
  • Mammography
  • Microcalcifications
  • Multi-view analysis


Dive into the research topics of 'Multi-view probabilistic classification of breast microcalcifications'. Together they form a unique fingerprint.

Cite this