  The problem of artifacts in whole slide image acquisition, prevalent in both
clinical workflows and research-oriented settings, necessitates human
intervention and re-scanning. Overcoming this challenge requires developing
quality control algorithms, that are hindered by the limited availability of
relevant annotated data in histopathology. The manual annotation of
ground-truth for artifact detection methods is expensive and time-consuming.
This work addresses the issue by proposing a method dedicated to augmenting
whole slide images with artifacts. The tool seamlessly generates and blends
artifacts from an external library to a given histopathology dataset. The
augmented datasets are then utilized to train artifact classification methods.
The evaluation shows their usefulness in classification of the artifacts, where
they show an improvement from 0.10 to 0.01 AUROC depending on the artifact
type. The framework, model, weights, and ground-truth annotations are freely
released to facilitate open science and reproducible research.
