On generating cell exemplars for detection of mitotic cells in breast cancer histopathology images

Mitotic activity is one of the main criteria that pathologists use to decide the grade of the cancer. Computerised mitotic cell detection promises to bring efficiency and accuracy into the grading process. However, detection and classification of mitotic cells in breast cancer histopathology images is a challenging task because of the large intra-class variation in the visual appearance of mitotic cells in various stages of cell division life cycle. In this paper, we test the hypothesis that cells in histopathology images can be effectively represented using cell exemplars derived from sub-images of various kinds of cells in an image for the purposes of mitotic cell classification. We compare three methods for generating exemplar cells. The methods have been evaluated in terms of classification performance on the MITOS dataset. The experimental results demonstrate that eigencells combined with support vector machines produce reasonably high detection accuracy among all the methods.

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