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Descripción
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| 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. | |
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Internacional
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Si |
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JCR del ISI
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No |
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Título de la revista
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Advances in Intelligent Systems and Computing |
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ISSN
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978-3-319-60815-0 |
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Factor de impacto JCR
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Información de impacto
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Volumen
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616 |
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DOI
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10.1007/978-3-319-60816-7_17 |
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Número de revista
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Desde la página
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137 |
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Hasta la página
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145 |
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Mes
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SIN MES |
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Ranking
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