UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading

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.

PDF Abstract


Introduced in the Paper:

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Colorectal Polyps Characterization UNITOPATHO Multi-resolution Balanced Accuracy 0.89 # 1


No methods listed for this paper. Add relevant methods here