  While data-driven approaches excel at many image analysis tasks, the
performance of these approaches is often limited by a shortage of annotated
data available for training. Recent work in semi-supervised learning has shown
that meaningful representations of images can be obtained from training with
large quantities of unlabeled data, and that these representations can improve
the performance of supervised tasks. Here, we demonstrate that an unsupervised
jigsaw learning task, in combination with supervised training, results in up to
a 9.8% improvement in correctly classifying lesions in colonoscopy images when
compared to a fully-supervised baseline. We additionally benchmark improvements
in domain adaptation and out-of-distribution detection, and demonstrate that
semi-supervised learning outperforms supervised learning in both cases. In
colonoscopy applications, these metrics are important given the skill required
for endoscopic assessment of lesions, the wide variety of endoscopy systems in
use, and the homogeneity that is typical of labeled datasets.
