ArticleBMC medical informatics and decision making2021
Development and operation of a digital platform for sharing pathology image data.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A new era in computational pathology: A survey on foundation and vision-language models.Journal of pathology informatics · 2026Review
- Meta-encoder: a unified integration framework for multiple pathological foundation models in cancer detection.Nature communications · 2026Article
- A new, machine learning-based approach to metastatic neuroendocrine tumors of unknown origin.Journal of neuroendocrinology · 2026Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Intelligent oncology: The convergence of artificial intelligence and oncology.Journal of the National Cancer Center · 2023Review
- Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions.Frontiers in oncology · 2023Review
- Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images.Diagnostics (Basel, Switzerland) · 2022Article
- Application of artificial intelligence in COVID-19 medical area: a systematic review.Journal of thoracic disease · 2021Review
- Artificial intelligence-based analysis and diagnosis of intradural extramedullary spinal tumors by stimulated Raman histology.Neuro-oncology advancesArticle
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists.
methodsOver 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists' workload, AI-assisted annotation was established in collaboration with university AI teams.
resultsA web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models. DISCUSSION: Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition.
conclusionsDespite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future.
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