Weakly-Supervised Classification with Mixture Models for Cervical Cancer Detection

摘要

The human supervision is required nowadays in many scientific applications but, due to the increasing data complexity, this kind of supervision has became too difficult or expensive and is no longer tenable. This paper therefore focuses on weakly-supervised classification which uses contextual informations to label the learning observations and to build a supervised classifier. This new kind of classification is treated in this work with a mixture model approach. For this, the problem of weakly-supervised classification is recasted in a problem of supervised classification with uncertain labels. The proposed approach is applied to cervical cancer detection for which the human supervision is very difficult and promising results are observed.