  Histopathological characterization of colorectal polyps allows to tailor
patients' management and follow up with the ultimate aim of avoiding or
promptly detecting an invasive carcinoma. Colorectal polyps characterization
relies on the histological analysis of tissue samples to determine the polyps
malignancy and dysplasia grade. Deep neural networks achieve outstanding
accuracy in medical patterns recognition, however they require large sets of
annotated training images. We introduce UniToPatho, an annotated dataset of
9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-slide
images, meant for training deep neural networks for colorectal polyps
classification and adenomas grading. We present our dataset and provide
insights on how to tackle the problem of automatic colorectal polyps
characterization.
