Mitosis detection using generic features and an ensemble of cascade adaboosts

作者：Faik Boray TEK · 发表于：Journal of Pathology Informatics · 年份：2013 · DOI：10.4103/2153-3539.112697 · 被引用次数：63 · 研究领域：AI in cancer detection、Breast Cancer Treatment Studies、Breast Lesions and Carcinomas

CONTEXT: Mitosis count is one of the factors that pathologists use to assess the risk of metastasis and survival of the patients, which are affected by the breast cancer. AIMS: We investigate an application of a set of generic features and an ensemble of cascade adaboosts to the automated mitosis detection. Calculation of the features rely minimally on object-level descriptions and thus require minimal segmentation. MATERIALS AND METHODS: The proposed work was developed and tested on International Conference on Pattern Recognition (ICPR) 2012 mitosis detection contest data. STATISTICAL ANALYSIS USED: We plotted receiver operating characteristics curves of true positive versus false positive rates; calculated recall, precision, F-measure, and region overlap ratio measures. RESULTS: WE TESTED OUR FEATURES WITH TWO DIFFERENT CLASSIFIER CONFIGURATIONS: 1) An ensemble of single adaboosts, 2) an ensemble of cascade adaboosts. On the ICPR 2012 mitosis detection contest evaluation, the cascade ensemble scored 54, 62.7, and 58, whereas the non-cascade version scored 68, 28.1, and 39.7 for the recall, precision, and F-measure measures, respectively. Mostly used features in the adaboost classifier rules were a shape-based feature, which counted granularity and a color-based feature, which relied on Red, Green, and Blue channel statistics. CONCLUSIONS: The features, which express the granular structure and color variations, are found useful for mitosis detection. The ensemble of adaboost...