ABSTRACT Accurate artefact classification is essential for reliable diagnosis and computer‐aided analysis in endoscopy, as visual artefacts can severely degrade image quality. However, endoscopic artefact classification is often hindered by limited and imbalanced datasets. This letter introduces an artefact localisation loss (ALL) that guides StyleGAN2 with adaptive discriminator augmentation (StyleGAN2‐ADA) to focus on artefact regions, thereby improving the realism of synthetic images generated from small datasets. The synthetic images were then used for data augmentation, which significantly enhanced classification performance, particularly for underrepresented artefact classes. Experiments on in‐house and endoscopy artefact detection challenge (EAD2020) datasets achieved lower FID scores and showed consistent improvements in F1‐scores, demonstrating the effectiveness of the proposed ALL and synthetic augmentation for robust artefact classification.
Chang et al. (Thu,) studied this question.