A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI

Abstract

Gastric cancer (GC) is the third leading cause of cancer death worldwide. Its clinical course varies considerably due to the highly heterogeneous tumour microenvironment (TME). Decomposing the complex TME from histological images into its constituent parts is crucial for evaluating its patterns and enhancing GC therapies. Although various deep learning methods were developed in medical field, their applications on this task are hindered by the lack of well-annotated histological images of GC. Through this work, we seek to provide a large database of histological images of GC completely annotated for 8 tissue classes in TME. The dataset consists of nearly 31 K histological images from 300 whole slide images. Additionally, we explained two deep learning models used as validation examples using this dataset.