  Survival rates for colorectal cancer are higher when polyps are detected at
an early stage and can be removed before they develop into malignant tumors.
Automated polyp detection, which is dominated by deep learning based methods,
seeks to improve early detection of polyps. However, current efforts rely
heavily on the size and quality of the training datasets. The quality of these
datasets often suffers from various image artifacts that affect the visibility
and hence, the detection rate. In this work, we conducted a systematic analysis
to gain a better understanding of how artifacts affect automated polyp
detection. We look at how six different artifact classes, and their location in
an image, affect the performance of a RetinaNet based polyp detection model. We
found that, depending on the artifact class, they can either benefit or harm
the polyp detector. For instance, bubbles are often misclassified as polyps,
while specular reflections inside of a polyp region can improve detection
capabilities. We then investigated different strategies, such as a learning
without forgetting framework, to leverage artifact knowledge to improve
automated polyp detection. Our results show that such models can mitigate some
of the harmful effects of artifacts, but require more work to significantly
improve polyp detection capabilities.
