  The advance of computer-aided detection systems using deep learning opened a
new scope in endoscopic image analysis. However, the learning-based models
developed on closed datasets are susceptible to unknown anomalies in complex
clinical environments. In particular, the high false positive rate of polyp
detection remains a major challenge in clinical practice. In this work, we
release the FPPD-13 dataset, which provides a taxonomy and real-world cases of
typical false positives during computer-aided polyp detection in real-world
colonoscopy. We further propose a post-hoc module EndoBoost, which can be
plugged into generic polyp detection models to filter out false positive
predictions. This is realized by generative learning of the polyp manifold with
normalizing flows and rejecting false positives through density estimation.
Compared to supervised classification, this anomaly detection paradigm achieves
better data efficiency and robustness in open-world settings. Extensive
experiments demonstrate a promising false positive suppression in both
retrospective and prospective validation. In addition, the released dataset can
be used to perform 'stress' tests on established detection systems and
encourages further research toward robust and reliable computer-aided
endoscopic image analysis. The dataset and code will be publicly available at
http://endoboost.miccai.cloud.
