Breast Cancer Assistant Diagnosis by Combining Cost Sensitive Feature Selection with Semi-supervised Learning
Songcan Chen · 2008
Masses and microcalcification clusters are the main characteristics in the digital mammography of breast cancer.It is traditionally thought that the features extracted from the masses and microcalcification clusters are always correct and effective,and therefore used for a supervised design of classifier to diagnose.In practice,however,one cannot necessarily promise effectiveness of the features.Furthermore,not all labels of the samples can be obtained due to the expensive labeling cost.In this paper,we design a novel diagnosis method for microcalcification clusters.The proposed method first uses an algorithm of modified cost sensitive selective ensemble(CSSE) to select the features that are most useful for classification and without redundant information.Then we design a semi-supervised consistent co-training(CoCo-Training) algorithm as a diagnosis classifier by taking sufficient advantage of the unlabeled samples.Experiments on the benchmark DDSM show that the proposed diagnose method outperforms others.