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## Description
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This data set represents a collection of textures in histological images of human colorectal cancer.
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It contains 5000 histological images of 150 * 150 px each (74 * 74 µm). Each image belongs to exactly one of eight tissue categories
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## Image format
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All images are RGB, 0.495 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x.
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## Ethics statement
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All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA).
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## More information / data usage
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For more information, please refer to the following article. Please cite this article when using the data set. \
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[Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zollner F:
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## Contact
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## Description
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This data set represents a collection of textures in histological images of human colorectal cancer.
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It contains 5000 histological images of 150 * 150 px each (74 * 74 µm). Each image belongs to exactly one of eight tissue categories.
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## Image format
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All images are RGB, 0.495 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x.
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Histological samples are fully anonymized images of formalin-fixed paraffin-embedded human colorectal adenocarcinomas (primary tumors) from our pathology archive
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(Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany).
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## Ethics statement
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All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA).
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The institutional ethics board waived the need for informed consent for this retrospective analysis of anonymized samples.
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All experiments were carried out in accordance with the approved guidelines and with the Declaration of Helsinki.
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## More information / data usage
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For more information, please refer to the following article. Please cite this article when using the data set. \
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[Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zollner F:
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Multi-class texture analysis in colorectal cancer histology (2016), Scientific Reports (in press)](https://doi.org/10.1038/srep27988)
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## Contact
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