  Colorectal Cancer(CRC) poses a great risk to public health. It is the third
most common cause of cancer in the US. Development of colorectal polyps is one
of the earliest signs of cancer. Early detection and resection of polyps can
greatly increase survival rate to 90%. Manual inspection can cause
misdetections because polyps vary in color, shape, size and appearance. To this
end, Computer-Aided Diagnosis systems(CADx) has been proposed that detect
polyps by processing the colonoscopic videos. The system acts a secondary check
to help clinicians reduce misdetections so that polyps may be resected before
they transform to cancer. Polyps vary in color, shape, size, texture and
appearance. As a result, the miss rate of polyps is between 6% and 27% despite
the prominence of CADx solutions. Furthermore, sessile and flat polyps which
have diameter less than 10 mm are more likely to be undetected. Convolutional
Neural Networks(CNN) have shown promising results in polyp segmentation.
However, all of these works have a supervised approach and are limited by the
size of the dataset. It was observed that smaller datasets reduce the
segmentation accuracy of ResUNet++. We train a U-Net to inpaint randomly
dropped out pixels in the image as a proxy task. The dataset we use for
pre-training is Kvasir-SEG dataset. This is followed by a supervised training
on the limited Kvasir-Sessile dataset. Our experimental results demonstrate
that with limited annotated dataset and a larger unlabeled dataset,
self-supervised approach is a better alternative than fully supervised
approach. Specifically, our self-supervised U-Net performs better than five
segmentation models which were trained in supervised manner on the
Kvasir-Sessile dataset.
