Mitosis Detection in Breast Cancer Using Superpixels and Ensemble Classifiers
- César A. Ortiz Toro
- Consuelo Gonzalo Martín
- Angel García Pedrero
- Alejandro Rodriguez Gonzalez
- Ernestina Menasalvas
- Fernández Riverola, Florentino (ed. lit.)
Editorial: Springer Suiza
ISBN: 978-3-319-60815-0
Año de publicación: 2017
Páginas: 137-145
Congreso: Practical Applications of Computational Biology & Bioinformatics (PACBB). International Conference (11. 2017. null)
Tipo: Aportación congreso
Resumen
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