  Endoscopic examinations are used to inspect the throat, stomach and bowel for
polyps which could develop into cancer. Machine learning systems can be trained
to process colonoscopy images and detect polyps. However, these systems tend to
perform poorly on objects which appear visually small in the images. It is
shown here that combining the single-shot detector as a region proposal network
with an adversarially-trained generator to upsample small region proposals can
significantly improve the detection of visually-small polyps. The Dynamic
SSD-GAN pipeline introduced in this paper achieved a 12% increase in
sensitivity on visually-small polyps compared to a conventional FCN baseline.
