General project information

  • Title: Bayesian Decision Support in Medical Screening
  • Acronym : B-SCREEN
  • Time frame: 2006 - 2009
  • Funded by NWO under BRICKS/FOCUS grant number 642.066.605

Project outline


In 2006 digitisation of the Dutch breast cancer screening has started. All screening mammograms will be stored in one national archive, which will be facilitated by the use of broadband technology. As a consequence, a large database of breast cancer cases will become available in a few years. This provides a unique opportunity for the development of decision-support in this domain. A major cause of missing breast cancer cases is interpretation failure. There is strong evidence that interpretation failure is a more common cause of missing significant lesions in screening than perceptual oversights. From audits it is known that in the Netherlands more than 25% of all cancers detected in the screened population show relatively clear signs of abnormality on previous screening mammograms, while another 25% show minimal signs.

There is evidence that computer-assisted detection (CAD) of lesions in mammograms can be of help to radiologists in interpreting whether a lesion is malignant or benign. The aim of this project is to use Bayesian networks and Bayesian classifiers to further address the problem of interpretation failures by radiologists. However, interpretation of lesions requires more medical background knowledge than is currently taken into account in CAD systems. This problem is addressed by a tight collaboration between radiologists and computer scientists.

  • Understanding the task of tumour mass detection in mammograms.
  • Design of a computer-based language for the representation of radiological background knowledge.
  • Improvement of classification performance of computer-aided detection software using Bayesian networks.


Image Analysis of Mammograms

This subproject focuses on improving feature extraction in mammograms. Detection of breast cancer in mammograms can be modelled as a multi-stage process. In a first step a search is carried out to identify locations of interest in the images. Sensitive methods for automating this step have been developed in the past and will be used in this project. These methods comprise detection of masses, microcalcifications, architectural distortion, and asymmetry, which are all signs of breast cancer. There is a need for the further development of image feature extraction and standardised data representation based on classification of local image features in single views. By combining information extracted from different views, we hope to be able to improve interpretation of mammograms.

Learning Bayesian Networks from Data

This subproject focuses on the development of methods that allow incorporation of radiological background knowledge in constructing Bayesian networks. Background knowledge is expected to play a role both in the elucidation of the appropriate Bayesian network topology as in finding appropriate context-specific dependence information. Relational probabilistic models and similarity networks have been chosen so far as a starting point for this line of research.

Observer Studies and Estimation of the Value of CAD for Radiologists

In this subproject we aim to obtain insight into the nature of the task of detection of breast lesions, suspected of cancer. We have established a good working relationship with Preventicon (breast cancer screening foundation in Utrecht, the Netherlands) and are now collaborating with radiologists in findings out which features and combinations of features, when detected, may help radiologist in reducing the misinterpretation error.

Project partners and collaborations


Publications / Presentations

  • M. Velikova, P.J.F. Lucas and N. Karssemeijer. "Using local context information to improve automatic mammographic mass detection", in: Studies in Health Technology and Informatics, volume 160 of MEDINFO 2010 - Proceedings of the 13th World Congress on Medical Informatics, 2010, pages 1291-1295. Abstract/PDF DOI PMID

  • M. Velikova, N. Ferreira, M. Samulski, P. Lucas and N. Karssemeijer. "An Advanced Probabilistic Framework for Assisting Screening Mammogram Interpretation", 2010. Abstract/PDF DOI

  • M. Velikova, M. Samulski, P.J.F. Lucas and N. Karssemeijer. "Improved mammographic CAD performance using multi-view information: a Bayesian network framework", Physics in Medicine and Biology 2009;54:1131-1147. Abstract/PDF DOI PMID

  • N. de Carvalho Ferreira, M.Velikova and P. Lucas. "Bayesian Modelling of Multi-View Mammography", in: ICML workshop: Machine Learning for Health Care Applications, 2008. PDF

  • M. Samulski and N. Karssemeijer. "Matching mammographic regions in mediolateral oblique and cranio caudal views: a probabilistic approach", in: Medical Imaging, volume 6915 of Proceedings of the SPIE, 2008, page 69151M. Abstract/PDF DOI

  • M. Velikova, H. Daniels and M. Samulski. "Partially Monotone Networks Applied to Breast Cancer Detection on Mammograms", in: ICANN '08: Proceedings of the 18th international conference on Artificial Neural Networks, Part I, 2008, pages 917-926. Abstract/PDF DOI

  • M. Velikova, P.J.F. Lucas, N. Ferreira, M. Samulski and N. Karssemeijer. "A decision support system for breast cancer detection in screening programs", in: Proceeding of the 2008 conference on ECAI 2008, 2008, pages 658-662. Abstract

  • M. Velikova, M. Samulski, N. Karssemeijer and P. Lucas. "Toward Expert Knowledge Representation for Automatic Breast Cancer Detection", in: AIMSA '08: Proceedings of the 13th international conference on Artificial Intelligence, 2008, pages 333-344. Abstract/PDF DOI

  • M. Velikova, N. de Carvalho Ferreira and P. Lucas. "Bayesian Network Decomposition for Modeling Breast Cancer Detection", in: Proceedings of the 11th Conference on Artificial Intelligence in Medicine, volume 4594 of Lecture Notes in Artificial Intelligence, 2007. Abstract/PDF DOI

  • M. Samulski, N. Karssemeijer, P. Lucas and P. Groot. "Classification of mammographic masses using support vector machines and Bayesian networks", in: Medical Imaging, volume 6514 of Proceedings of the SPIE, 2007, page 65141J. Abstract/PDF DOI

For more publications go to [Publications] or to the personal web-pages of the project researchers.

In Press