HistoArtifacts

This dataset contains five notable histological artifacts: blur, blood (hemorrhage), air bubbles, folded tissue, and damaged tissue. This dataset is used in the following works, and a description of the dataset can be found at https://arxiv.org/abs/2403.07743.

The full dataset is explained and used in  the article, "Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs.". https://arxiv.org/abs/2403.07743

See the detailed video explanation behind the motivation of artifact detection in computational pathology. in the video paper: "Extract, detect, eliminate: Enhancing reliability and performance of computational pathology through artifact processing pipelines" https://www.sciencetalks-journal.com/article/S2772-5693(24)00013-6/fulltext

Please cite the following papers while using the dataset, in full or partially:. 

A sub-dataset contains folded tissues extracted at 20x and blur class used in the paper "Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images".  https://www.sciencedirect.com/science/article/pii/S0895611123001398

A sub-dataset using air bubbles is used in the paper: "Vision transformers for small histological datasets learned through knowledge distillation" https://link.springer.com/chapter/10.1007/978-3-031-33380-4_13https://arxiv.org/abs/2305.17370

A sub-dataset using blood and damaged tissue is used in the paper: "Quantifying the effect of color processing on blood and damaged tissue detection in whole slide images" https://ieeexplore.ieee.org/abstract/document/9816283

"Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs.". https://arxiv.org/abs/2403.07743

"The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review" https://ieeexplore.ieee.org/document/9777677