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

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).