ID: 3520174 SEMI-AUTOMATED ANNOTATION TOOL OUTPERFORMS TRAINED MEDICAL STUDENTS AND IS COMPARABLE TO CLINICAL EXPERT PERFORMANCE FOR FRAME-LEVEL DETECTION OF COLORECTAL POLYPS

Training of deep learning systems requires an enormous amount of labeled data. This data must ideally cover the entire range of polyp appearances in real life, but also the whole possible range of image qualities and polyp locations. Expert labelling of each frame in a polyp video is therefore the most robust way for constructing a training set, but this is very time-consuming and currently represents a major barrier for widespread...