Pathologist's Annotated Image Tiles for Multi-Class Tissue Classification in Colorectal Cancer

<strong>Content</strong> The present dataset is related to a study aiming to identify the best method to perform multi-tissue classification from digital histological images. Histological images, completely anomized, come from formalin-fized paraffine-embedded sample of a patient affected by colorectal cancer. Two directories are available: “CRC_image_tiles.zip”: a zipped folder containing tiles (n=5984) annotated by a pathologist, grouped in 7 subdirectories, each of them representing a class ( 150 * 150 px). “Macenko_normalized_CRC_image_tiles.zip”: Macenko-normalized tiles (n=5984) annotated by a pathologist, grouped in 7 subdirectories, each of them representing a class ( 150 * 150 px). <strong>Ethical Statement</strong> The study has been funded by “Tecnopolo per la Medicina di Precisione (CUP B84I18000540002)”. The institutional Ethic Committee approved the study (Prot n. 780/CE). <strong>Info and Data Usage </strong> For further details concerning the aforementioned dataset, refer to the paper below. Please cite the following articles if you need this dataset for your research. Altini N. et al. (2021) Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features. In: Huang DS., Jo KH., Li J., Gribova V., Bevilacqua V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_42 Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., ... &amp; Bevilacqua, V. (2023). The Role of Unpaired Image-to-Image Translation for Stain Color Normalization in Colorectal Cancer Histology Classification. <em>Computer Methods and Programs in Biomedicine</em>, 107511. <br> https://doi.org/10.1016/j.cmpb.2023.107511