Self-training with unlabeled regions for NBI image recognition

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.