Transductive cost-sensitive lung cancer image classification
Applied Intelligence
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In lung cancer image classification, the label concepts are usually given out for the whole image but not for a single cell, which leads to a low predict accuracy if we use supervised learning methods on cell-level. In this paper, we model lung cancer image classification as a multi-class multi-instance learning problem. A lung cancer image is treated as a bag. Each bag contains a set of instances that are lung cancer cells. In our approach, we first extract the features for cells in all images as bags, and then transform each bag into a new bag feature space by computing the Hausdorff distance in all of the bags. At last we use AdaBoost algorithm to select the bag features and build two-level classifiers to solve the multi-class classification problem. Experiments on the lung cancer image dataset show that our approach is an effective solution for the lung cancer classification problem.