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