Probabilistic Relational Modelling of Mammographic Images

Computer-aided detection (CAD) is used in medical science as a means of supporting a doctor's observations and interpretations. While X-ray imaging techniques, such as mammography, yield a great deal of information, it is not always easy to evaluate detected mammographic regions as being suspicious for cancer, which results in a number of cancers to be misinterpreted or missed in an image. In this sense, CAD systems have as aim the increase of detection rates when analysing mammograms, by identifying features that are characteristic for breast cancer. In this research we aim at using the features extracted from mammographic images in order to analyse the development of suspicious lesions. Differently from other breast cancer models, the data modelling exploits object orientation. This allows not only for a natural description of domain entities and their intrinsic relations, but also the application of relational learning techniques, which handles our heterogeneous data instances both in terms of learning and inference.