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Polyp Detection via Imbalanced Learning and Discriminative Feature Learning | IEEE Journals & Magazine | IEEE Xplore

Polyp Detection via Imbalanced Learning and Discriminative Feature Learning


Abstract:

Recent achievement of the learning-based classification leads to the noticeable performance improvement in automatic polyp detection. Here, building large good datasets i...Show More

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-intensive 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 framework to learn an unbiased polyp detector from the imbalanced datasets. In our learning scheme, we learn multiple weak classifiers with the datasets rebalanced by up/down sampling, and generate a polyp detector by combining them. In addition, for enhancing discriminability between polyps and non-polyps that have similar appearances, we propose an effective feature learning method using partial least square analysis, and use it for learning compact and discriminative features. Experimental results using challenging datasets show obvious performance improvement over other detectors. We further prove effectiveness and usefulness of the proposed methods with extensive evaluation.
Published in: IEEE Transactions on Medical Imaging ( Volume: 34, Issue: 11, November 2015)
Page(s): 2379 - 2393
Date of Publication: 18 May 2015

ISSN Information:

PubMed ID: 26011864

References

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