Polyp detection and segmentation from endoscopy images

Abstract

Endoscopy is a widely used clinical procedure for the detection of different diseases in internal gastrointestinal tract’s organs such as the stomach and colon. Modern endoscopes allow getting high-quality video during the procedure. Computer-assisted methods might support medical specialists in detecting or segmenting anomaly regions on the picture. Many datasets are available and methods to detect polyp regions have been proposed. One kind of task is polyps segmentation on images and videos. The best results in semantic segmentation of polyps are now achieved with fully supervised approaches. In this thesis, we describe experiments with CaraNet model. We checked robustness on cross-validation on several publicly available datasets and small private dataset, tried a few modifications of attention layer in order to improve performance, presented and discussed results.