ArticleScientific data2025
A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Universal and transferable attacks on pathology foundation models using microscopic perturbations.Light, science & applications · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Generative AI for misalignment-resistant virtual staining to accelerate histopathology workflows.Nature communications · 2026Article
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
- An interpretable deep learning framework for intestinal metaplasia detection in gastric histopathology images.Frontiers in oncology · 2026Article
- A multi-task and explainable swin transformer framework for cross-scale computational pathology in gastrointestinal cancer.Frontiers in oncology · 2026Article
- Large language models driven neural architecture search for universal and lightweight disease diagnosis on histopathology slide images.NPJ digital medicine · 2025Article
- STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology.ArXiv · 2025Article
- Nanomaterials for Theranostic Management of Oral Cancer: Advances in Imaging, Biosensing, Targeted Delivery, and Multimodal Synergistic Therapy.International journal of nanomedicine · 2025Review
- STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology.Advances in neural information processing systems · 2025Article
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Authors and funding
12 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.