Category Archives: International

Polyp Detection via Imbalanced Learning and Discriminative Feature Learning– Author: Seung-Hwan Bae and Kuk-Jin Yoon – Published Date: May 18, 2015 – Category: Tracking and Detection – Place of publication: IEEE Transactions on Medical Imaging Abstract Recent achievement of the learning based classification leads to the noticeable performance improvement in automatic polyp detection. Here, building large good datasets is very crucial for learning a reliable detector. However, it is practically challenging due to the diversity of polyp types, expensive inspection, and labor- ntensive labeling tasks. For this reason, the polyp datasets usually tend to be imbalanced, i.e. the number of non polyp samples is much larger than that of polyp samples, and learning with those imbalanced datasets results in a detector biased toward a non polyp class. In this paper, we propose a data sampling based boosting frameworkto learn an unbiased polyp detector from the imbalanced datasets. In our learning…