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In the diagnosis of autoimmune diseases, an important task is to classifyimages of slides containing several HEp-2 cells. All cells from one slide sharethe same label, and by classifying cells from one slide independently, someinformation on the global image quality and intensity is lost. Considering onewhole slide as a collection (a bag) of feature vectors, however, poses theproblem of how to handle this bag. A simple, and surprisingly effective,approach is to summarize the bag of feature vectors by a few quantile valuesper feature. This characterizes the full distribution of all instances, therebyassuming that all instances in a bag are informative. This representation isparticularly useful when each bag contains many feature vectors, which is thecase in the classification of the immunofluorescence images. Experiments on theclassification of indirect immunofluorescence images show the usefulness ofthis approach.

Quantile Representation for Indirect Immunofluorescence Image Classification