Figure 1
From: An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy
The three sub-challenges of the Endoscopy Artefact Detection (EAD) challenge. (a) The “detection” task is aimed at coarse localization and classification of each image artefact. Given an input image (left) a detection model (middle) outputs the artefact class and coordinates of the containing bounding box defined by the top left (x1, y1) and bottom right corners (x2, y2) of the box (right). (b) The “segmentation” task is aimed at finer spatial localization through the precise delineation of artefact boundaries. Given an input image (left), a segmentation model outputs binary images (right) denoting the presence (‘1’) or absence (‘0’) of each artefact class. (c) The “out-of-sample generalization” task is aimed at assessing the ability of a model (model1) trained on one dataset (dataset1) model1 (left) to detect artefacts in an unseen dataset (dataset2) comprising the same set of class labels but with different data attributes such as data modalities or instrument or acquisition center or a combination of factors.
