MODELING OF BREAST CANCER DIAGNOSIS BY CONTEXTUAL GRAPHS

Content-Based Image Retrieval (CBIR) systems for diagnosis support, requires not only new tools but also new concepts and new approaches. Taking into account the domain knowledge is very challenging because of the particular importance of every detail surrounding any given topic, user, task and user’s query. However, tackling domain knowledge in an abstract way is not sufficient: it is important also to tackle the context of use of this domain knowledge, and modeling context becomes the key problem. This problem has been encountered in artificial intelligence and fixed with the development of a conceptual framework for context modeling and the design and development of a context-based formalism of representation called “Contextual Graphs”. This paper presents a preliminary modeling of breast cancer diagnosis in Contextual Graphs. We reproduce the modeling realized by Logan-Young and Hoffman (1995) in a contextbased representation, and we discuss extensions offered by such a representation as an incremental enrichment by new knowledge and learning of new practices by the system, a presentation at different granularity, and the generation of various explanations.