This dataset is a subset of the Camelyon-17 Breast Cancer Challenge. It contains 224x224 H&E histological image patches where blood has been detected. It was originally sampled to validate the blood detection capabilities of the method presented in [1]. Blood was manually identified by a trained technician. If you use this dataset, please cite: Pérez-Bueno, F., Engan, K., Molina, R. (2024). Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD. In: Journal of Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2024.102969 [bibtex] Pérez-Bueno, F., Engan, K., Molina, R. (2023). A Robust BKSVD Method for Blind Color Deconvolution and Blood Detection on H&E Histological Images. In: Artificial Intelligence in Medicine. AIME 2023, vol 13897. https://doi.org/10.1007/978-3-031-34344-5_25 [bibtex] and the original publication for the Camelyon-17 Challenge (see details on the challenge website) Summary: 25 images from center_0 7786 tissue patches 527 blood patches 104 patches of other artifacts (such as blur, folded tissue, image borders, cauterized, etc. Not labeled) The folder structure is as follows: center/image_id/pathology_label/patch_label/ pathology_label can take the following values: annotated: the patch comes from a tumor annotated region (see details in Camelyon-17 Challenge) no_annotated: the patch comes from a non-tumor slide (negative stage label) unknown: the patch comes from a slide with a tumor stage label which is not annotated. patch_label can take the following values: tissue: no blood or less than ~25% of blood blood: more than ~25% blood other: the patch has a significant amount (>~25%) of pixels that are nor tissue nor blood. Patches are sampled at the maximum resolution available 40x, and the filename includes the starting pixel in the x and y dimension. For the original .tiff images at high quality, please refer to the Camelyon-17 Challenge. The license for this dataset is CC0 following the Camelyon-17 license.