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)., The dataset source was 90 formalin-fixed paraffin-embedded hematoxylin-and-eosin-stained histopathology slides with 4-μm-thick sections of urinary bladder lesions that were either cystitis (43 slides) or UCC (47 slides). Slides were obtained from 74 specimens from the Departments of Pathology of both the Faculty of Medicine and the Cancer Institute in our university. The UCC slides were of different pathological stages: pTa (five slides), pT1 (nine slides), pT2 (28 slides), and pT3a (five slides). Slides were photographed using an Olympus® E-330 digital camera mounted on an Olympus® CX31 light microscope by an Olympus® E330-ADU1.2X adapter. Magnification of the microscope was set to 20x. Certain camera settings were adjusted before photographing. The shutter speed, aperture value, International Organization for Standardization (ISO) sensitivity to light, and white balance were set automatically. Exposure compensation value, which controls the brightness, was set to +1.0. Images were set..., , # Hematoxylin-and-eosin-stained bladder urothelial cell carcinoma versus inflammation digital histopathology image dataset

KEY WORDS

artificial intelligence; cystitis; deep learning; histopathology; urothelial cell carcinoma

BACKGROUND

Machine learning, a major branch of artificial intelligence, comprises algorithms that can make predictions after being trained on prior examples. Deep learning, a subset of machine learning, consists of a more recent and more sophisticated category of these algorithms. Deep learning includes, but is not limited to, convolutional neural networks, which are capable of directly learning from image datasets (1,2). This opened the door for a myriad of applications in medical image analysis (3). In digital pathology, these applications encompass low-level tasks such as nuclei segmentation, mitosis detection, and gland segmentation; standard applications such as tumor detection, subtyping, grading, and staging; and advanced inferences that cannot be relia...