Determining the severity and potential aggressiveness of breast cancer is an important step in the determination of the treatment options for a patient. Mitosis activity is one of the main components in breast cancer severity grading. Currently, mitosis counting is a laborious, prone to processing errors, done manually by a pathologist. This paper presents a novel approach for automatic mitosis detection, where promising candidates are selected from a superpixel segmentation of the image and classified using an ensemble classifier created from a selection from a pool of different color spaces, different features vector.
CITATION STYLE
Toro, C. A. O., Martín, C. G., Pedrero, A. G., Gonzalez, A. R., & Menasalvas, E. (2017). Mitosis detection in breast cancer using superpixels and ensemble classifiers. In Advances in Intelligent Systems and Computing (Vol. 616, pp. 137–145). Springer Verlag. https://doi.org/10.1007/978-3-319-60816-7_17
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