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Hematoxylin-and-eosin-stained bladder urothelial cell carcinoma versus inflammation digital histopathology image dataset

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Feb 28, 2023 version files 68.37 GB
Feb 28, 2023 version files 68.37 GB

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Abstract

Digital pathology requires a large number of well-annotated image datasets to benefit from deep learning algorithms. Unfortunately, most available datasets are annotated at the slide level; which is not as useful as patch-level or pixel-level annotations. Additionally, urinary bladder cancer is underrepresented in digital pathology deep learning studies. Here, we present an annotated dataset of patch-level images obtained from 90 hematoxylin-and-eosin-stained histopathology slides of urinary bladder lesions. Non-overlapping photographs of all available tissue areas on each slide were systematically obtained and manually classified by the pathologist in our team as inflammation (5,948 images), urothelial cell carcinoma (UCC) (5,811 images), or invalid (3,132 images).