Using a Multiple Experts System to Classify Fluorescence Intensity in Antinuclear Autoantibodies Analysis

At the present, Indirect Immunofluorescence (IIF) is the recommended method for the detection of antinuclear autoantibodies (ANA). IIF diagnosis requires estimating both fluorescent intensity and pattern description, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of Computer Aided Diagnosis (CAD) tools that can offer a support to physician decision. In this paper we firstly propose a strategy to reliably label the image data set by using the diagnoses performed by different physicians, and then we present a system to classify the fluorescent intensity. Such a system adopts a Multiple Experts System architecture (MES), based on the classifier selection paradigm. Two different selection rules are discussed. The approach has been successfully tested on an annotated database of IIF images.