UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
This addresses the need for large annotated datasets in medical imaging to improve automated diagnosis of colorectal polyps, which is incremental as it provides a new dataset rather than a novel method.
The authors tackled the problem of colorectal polyp classification and adenoma dysplasia grading by introducing UniToPatho, a labeled histopathological dataset of 9536 H&E stained patches from 292 whole-slide images, aimed at training deep neural networks for this task.
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.