Colorectal Cancer Histology Image Tiles and CycleGAN-based Normalization Model for Tissue Classification

<strong>Content</strong> The present dataset is linked to a research aimed at discovering the best normalization pipeline and classification model for colorectal cancer multi-class tissue classification.<br> The 15,856 histological image tiles are completely anomized and are extracted from 10 formalin-fized paraffine-embedded samples of patients affected by colorectal cancer. The materials are split in two folders: “CRC_Tiles_IRCCS_ISTITUTO_TUMORI_BARI.zip”: a zipped folder containing tiles (n=15,856) annotated by a pathologist, grouped in 6 subdirectories, each of them representing a class. Tiles are of size 224 x 224 px, taken at a resolution of 0.5 μm/px.<br> “tcga2tecno.zip”: CycleGAN-based normalization model for colorectal cancer tissue. It has to be used in conjunction with the following repository available on GitHub: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix. <br> <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). <br> <strong>Related Datasets and Works</strong><br> <br> For further details concerning the aforementioned dataset, refer to the paper below. <br> Please cite this article 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 Please also consider the dataset offered in our previous work: Altini N. et al. (2021). Pathologist's Annotated Image Tiles for Multi-Class Tissue Classification in Colorectal Cancer (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4785131