  The diagnosis process of colorectal cancer mainly focuses on the localization
and characterization of abnormal growths in the colon tissue known as polyps.
Despite recent advances in deep object localization, the localization of polyps
remains challenging due to the similarities between tissues, and the high level
of artifacts. Recent studies have shown the negative impact of the presence of
artifacts in the polyp detection task, and have started to take them into
account within the training process. However, the use of prior knowledge
related to the spatial interaction of polyps and artifacts has not yet been
considered. In this work, we incorporate artifact knowledge in a
post-processing step. Our method models this task as an inductive graph
representation learning problem, and is composed of training and inference
steps. Detected bounding boxes around polyps and artifacts are considered as
nodes connected by a defined criterion. The training step generates a node
classifier with ground truth bounding boxes. In inference, we use this
classifier to analyze a second graph, generated from artifact and polyp
predictions given by region proposal networks. We evaluate how the choices in
the connectivity and artifacts affect the performance of our method and show
that it has the potential to reduce the false positives in the results of a
region proposal network.
