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Endoscopic imaging is commonly used to diagnose Ulcerative Colitis (UC) andclassify its severity. It has been shown that deep learning based methods areeffective in automated analysis of these images and can potentially be used toaid medical doctors. Unleashing the full potential of these methods depends onthe availability of large amount of labeled images; however, obtaining andlabeling these images are quite challenging. In this paper, we propose a activelearning based generative augmentation method. The method involves generating alarge number of synthetic samples by training using a small dataset consistingof real endoscopic images. The resulting data pool is narrowed down by usingactive learning methods to select the most informative samples, which are thenused to train a classifier. We demonstrate the effectiveness of our methodthrough experiments on a publicly available endoscopic image dataset. Theresults show that using synthesized samples in conjunction with active learningleads to improved classification performance compared to using only theoriginal labeled examples and the baseline classification performance of 68.1%increases to 74.5% in terms of Quadratic Weighted Kappa (QWK) Score. Anotherobservation is that, attaining equivalent performance using only real datanecessitated three times higher number of images.

Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation