  Early detection of colorectal polyps is of utmost importance for their
treatment and for colorectal cancer prevention. Computer vision techniques have
the potential to aid professionals in the diagnosis stage, where colonoscopies
are manually carried out to examine the entirety of the patient's colon. The
main challenge in medical imaging is the lack of data, and a further challenge
specific to polyp segmentation approaches is the difficulty of manually
labeling the available data: the annotation process for segmentation tasks is
very time-consuming. While most recent approaches address the data availability
challenge with sophisticated techniques to better exploit the available labeled
data, few of them explore the self-supervised or semi-supervised paradigm,
where the amount of labeling required is greatly reduced. To address both
challenges, we leverage synthetic data and propose an end-to-end model for
polyp segmentation that integrates real and synthetic data to artificially
increase the size of the datasets and aid the training when unlabeled samples
are available. Concretely, our model, Pl-CUT-Seg, transforms synthetic images
with an image-to-image translation module and combines the resulting images
with real images to train a segmentation model, where we use model predictions
as pseudo-labels to better leverage unlabeled samples. Additionally, we propose
PL-CUT-Seg+, an improved version of the model that incorporates targeted
regularization to address the domain gap between real and synthetic images. The
models are evaluated on standard benchmarks for polyp segmentation and reach
state-of-the-art results in the self- and semi-supervised setups.
