  Recently, the amount of GI tract datasets is introduced more and more by
gathering from contests and challenges. The most common task needs to solve
that is to classify images from the GI tract into various classes. However, the
contributions of the existing approaches exhibit lots of limitations. In this
paper, we aim to develop a computer-aided diagnosis system to classify the
pathological findings in endoscopy images, the system can classify some common
pathologies including polyps, esophagitis, and ulcerative -- colitis. To
evaluate the proposed work, we use the public dataset which is Hyper--Kvasir
instead of gathering the data. The key idea of our system is to develop
self-supervised learning based on the Barlow Twins framework with a downstream
task which is an endoscopy image classification integrated with triplet loss
and focal loss functions. The self-supervision framework and focal loss
function are used to overcome class-imbalanced data, while the triplet loss
function is to tackle the domain-specific properties in endoscopy images which
are inter/intra-class problems. An extensive experimental study on the
pathological finding images in the Hyper--Kvasir dataset has shown that our
proposed system is in general better than the compared methods, whereas using a
simple neural network model. This means the proposed system can be used
efficiently and capable of accurately for the classification of pathology
images in the GI tract.
