Analytical and predictive quasi-supervised learning for cancer recognition in digital cytology

Abstract

In this work, cancer recognition in digital cytology data was carried out using quasi-supervised learning. The data subject to recognition contained ground-truth data only in the form of a labeled set of cancer-free samples and the cancerous samples were provided along with cancer-free samples in an unlabeled mixed dataset. In this framework, a predictive method was derived to label future samples as cancerous or cancer-free based on this data at hand together with an analytical method to label the cancerous samples in the mixed dataset. In the experiments, the methods based on the quasi-supervised learning algorithm achieved higher recognition performance in both cases than the alternative approaches based on supervised support vector machine classifiers. These results indicate that the quasi-supervised learning is the only valid approach in both analytical and predictive recognition when only labeled cancer-free samples are available for statistical learning.