  Early identification of a polyp in the lower gastrointestinal (GI) tract can
lead to prevention of life-threatening colorectal cancer. Developing
computer-aided diagnosis (CAD) systems to detect polyps can improve detection
accuracy and efficiency and save the time of the domain experts called
endoscopists. Lack of annotated data is a common challenge when building CAD
systems. Generating synthetic medical data is an active research area to
overcome the problem of having relatively few true positive cases in the
medical domain. To be able to efficiently train machine learning (ML) models,
which are the core of CAD systems, a considerable amount of data should be
used. In this respect, we propose the PolypConnect pipeline, which can convert
non-polyp images into polyp images to increase the size of training datasets
for training. We present the whole pipeline with quantitative and qualitative
evaluations involving endoscopists. The polyp segmentation model trained using
synthetic data, and real data shows a 5.1% improvement of mean intersection
over union (mIOU), compared to the model trained only using real data. The
codes of all the experiments are available on GitHub to reproduce the results.
