Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation
This addresses a domain-specific problem in histopathology by reducing the need for manual annotation, though it is incremental as it builds on existing augmentation techniques.
The paper tackles the problem of limited annotated data for artifact detection in whole slide images by proposing a method to augment images with artifacts from an external library, which improves artifact classification AUROC from 0.10 to 0.01 depending on the artifact type.
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.