  Disease severity regression by a convolutional neural network (CNN) for
medical images requires a sufficient number of image samples labeled with
severity levels. Conditional generative adversarial network (cGAN)-based data
augmentation (DA) is a possible solution, but it encounters two issues. The
first issue is that existing cGANs cannot deal with real-valued severity levels
as their conditions, and the second is that the severity of the generated
images is not fully reliable. We propose continuous DA as a solution to the two
issues. Our method uses continuous severity GAN to generate images at
real-valued severity levels and dataset-disjoint multi-objective optimization
to deal with the second issue. Our method was evaluated for estimating
ulcerative colitis (UC) severity of endoscopic images and achieved higher
classification performance than conventional DA methods.
