UNITOPATHO

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 stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading. The slides are acquired through a Hamamatsu Nanozoomer S210 scanner at 20× magnification (0.4415 μm/px). Each slide belongs to a different patient and is annotated by expert pathologists, according to six classes as follows: NORM- Normal tissue;HP- Hyperplastic Polyp;TA.HG- Tubular Adenoma, High-Grade dysplasia;TA.LG- Tubular Adenoma, Low-Grade dysplasia;TVA.HG- Tubulo-Villous Adenoma, High-Grade dysplasia;TVA.LG- Tubulo-Villous Adenoma, Low-Grade dysplasia. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111, DeepHealth Project.

对结直肠息肉的病理学特征描述旨在定制患者的管理与随访，其最终目的是避免或及时检测侵袭性癌变。结直肠息肉的特征描述依赖于组织样本的病理学分析，以确定息肉的恶性和异型增生等级。深度神经网络在医学模式识别中实现了卓越的准确性，然而它们需要大量标注的训练图像。本团队推出UniToPatho，这是一个由9536张苏木精-伊红染色切片组成的标注数据集，这些切片从292张全切片图像中提取，旨在训练深度神经网络进行结直肠息肉分类和腺瘤分级。切片通过Hamamatsu Nanozoomer S210扫描仪以20倍放大率（0.4415 μm/px）获取。每张切片均属于不同患者，并由专家病理学家根据以下六类进行标注：NORM-正常组织；HP-增生性息肉；TA.HG-管状腺瘤，高级别异型增生；TA.LG-管状腺瘤，低级别异型增生；TVA.HG-管状绒毛状腺瘤，高级别异型增生；TVA.LG-管状绒毛状腺瘤，低级别异型增生。此项目已获得欧洲联盟“地平线2020”研究与创新计划的资金支持，项目编号为825111，DeepHealth项目。