Artefact segmentation in digital pathology whole-slide images
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Dataset with examples of Artefacts in Digital Pathology. The dataset contains 22 Whole-Slide Images, with H&E or IHC staining, showing various types and levels of defect to the slides. Annotations were made by a biomedical engineer based on examples given by an expert. The dataset is split in different folders: train 18 whole-slide images (extracted at 1.25x & 2.5x magnification) All from the same Block (colorectal cancer tissue) 1/2 with H&E & 1/2 with anti-pan-cytokeratin IHC staining. validation 3 whole-slide images (1.25x + 2.5x mag) 2 from the same Block as the training set (1 IHC, 1 H&E) 1 from another Block (IHC anti-pan-cytokerating, gastroesophageal junction lesion) validation_tiles patches of varying sizes taken from the 3 validation whole-slide images @1.25x magnification. 7 patches from each slide. test 1 whole-slide image (1.25x + 2.5x mag) From another block: IHC staining (anti-NR2F2), mouth cancer For the train, validation and test whole-slide images, each slide has:<br> - The RGB images @1.25x & 2.5x mag<br> - The corresponding background/tissue masks<br> - The corresponding annotation masks containing examples of artefacts (note that a majority of artefacts are not annotated. In total, 918 artefacts are in the train set) For the validation tiles, the following table gives the "patch-level" supervision: tile# Artefact(s)<br> 00 None/Few<br> 01 Tear&Fold<br> 02 Ink<br> 03 None/Few<br> 04 None/Few<br> 05 Tear&Fold<br> 06 Tear&Fold + Blur<br> 07 Knife damage<br> 08 Knife damage<br> 09 Ink<br> 10 None/Few<br> 11 Tear&Fold<br> 12 Tear&Fold<br> 13 None/Few<br> 14 None/Few<br> 15 Knife damage<br> 16 Tear&Fold<br> 17 None/Few<br> 18 None/Few<br> 19 Blur<br> 20 Knife damage
