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 patt...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04489-9 |
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| _version_ | 1832586039712022528 |
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| author | Shenghan Lou Jianxin Ji Huiying Li Xuan Zhang Yang Jiang Menglei Hua Kexin Chen Kaiyuan Ge Qi Zhang Liuying Wang Peng Han Lei Cao |
| author_facet | Shenghan Lou Jianxin Ji Huiying Li Xuan Zhang Yang Jiang Menglei Hua Kexin Chen Kaiyuan Ge Qi Zhang Liuying Wang Peng Han Lei Cao |
| author_sort | Shenghan Lou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-221050e7585a4facb5d5c6b160b07d6c |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-221050e7585a4facb5d5c6b160b07d6c2025-01-26T12:14:52ZengNature PortfolioScientific Data2052-44632025-01-011211710.1038/s41597-025-04489-9A large histological images dataset of gastric cancer with tumour microenvironment annotation for AIShenghan Lou0Jianxin Ji1Huiying Li2Xuan Zhang3Yang Jiang4Menglei Hua5Kexin Chen6Kaiyuan Ge7Qi Zhang8Liuying Wang9Peng Han10Lei Cao11Department of Oncology Surgery, Harbin Medical University Cancer HospitalDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Pathology, Harbin Medical University Cancer HospitalDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Pathology, Harbin Medical University Cancer HospitalDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Pathology, Harbin Medical University Cancer HospitalDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityDepartment of Biostatistics, School of Public Health, Harbin Medical UniversitySchool of Health Management, Harbin Medical UniversityDepartment of Oncology Surgery, Harbin Medical University Cancer HospitalDepartment of Biostatistics, School of Public Health, Harbin Medical UniversityAbstract 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.https://doi.org/10.1038/s41597-025-04489-9 |
| spellingShingle | Shenghan Lou Jianxin Ji Huiying Li Xuan Zhang Yang Jiang Menglei Hua Kexin Chen Kaiyuan Ge Qi Zhang Liuying Wang Peng Han Lei Cao A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI Scientific Data |
| title | A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI |
| title_full | A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI |
| title_fullStr | A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI |
| title_full_unstemmed | A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI |
| title_short | A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI |
| title_sort | large histological images dataset of gastric cancer with tumour microenvironment annotation for ai |
| url | https://doi.org/10.1038/s41597-025-04489-9 |
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