Abstract:
In this paper, we propose a self-training method which uses unlabeled regions in the original images obtained from a colorectal Narrow Band Imaging (NBI) zoom-video endos...Show MoreMetadata
Abstract:
In this paper, we propose a self-training method which uses unlabeled regions in the original images obtained from a colorectal Narrow Band Imaging (NBI) zoom-video endoscope. The proposed method first trims a number of patches from unlabeled regions in the original images and uses them as unlabeled training samples. Classifiers are trained with the available labeled samples, as well as with those unlabeled training samples, using a newly-proposed rejection condition which takes into account the class asymmetry of the NBI images. Experimental results demonstrate that the proposed method improves performance with a statistically significant difference.
Date of Conference: 11-15 November 2012
Date Added to IEEE Xplore: 14 February 2013
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Conference Location: Tsukuba, Japan