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

被引：0

作者：

Lou, Shenghan ^{[1
]}

Ji, Jianxin ^{[2
]}

Li, Huiying ^{[3
]}

Zhang, Xuan ^{[2
]}

Jiang, Yang ^{[3
]}

Hua, Menglei ^{[2
]}

Chen, Kexin ^{[3
]}

Ge, Kaiyuan ^{[2
]}

Zhang, Qi ^{[2
]}

Wang, Liuying ^{[4
]}

Han, Peng ^{[1
,5
,6
]}

Cao, Lei ^{[2
]}

机构：

[1] Harbin Med Univ, Dept Oncol Surg, Canc Hosp, 150 Haping Rd, Harbin 150081, Heilongjiang, Peoples R China

[2] Harbin Med Univ, Sch Publ Hlth, Dept Biostat, Harbin 150081, Peoples R China

[3] Harbin Med Univ, Canc Hosp, Dept Pathol, 150 Haping Rd, Harbin 150081, Heilongjiang, Peoples R China

[4] Harbin Med Univ, Sch Hlth Management, Harbin 150081, Peoples R China

[5] Heilongjiang Prov Key Lab Mol Oncol, 150 Haping Rd, Harbin 150081, Heilongjiang, Peoples R China

[6] Heilongjiang Canc Inst, 150 Haping Rd, Harbin 150081, Heilongjiang, Peoples R China

来源：

SCIENTIFIC DATA | 2025年 / 12卷 / 01期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

CLASSIFICATION;

D O I：

10.1038/s41597-025-04489-9

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：7

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