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QuPath: Open source software for digital pathology image analysis
Open accessPublished version of a preprint
Abstract
QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath's flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.
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“On the software side, frameworks such as TensorFlow and PyTorch support AI model development, while tools like QuPath enable image annotation and analysis [44].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [44]
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“Emerging solutions include standardized open-source tools (e.g., QuPath) [44], collaborative multi-institutional annotation efforts, and AI-assisted annotation workflows that reduce manual burden [45].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [44]
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“The microglial ultrastructural analyses was performed using QuPath (v.0.4.4) [71] on images blinded to both animal and group, following an adaptation from our previous work [56, 64].”
From Sex chromosomes and sex hormones differently shape microglial properties during normal physiological conditions in the adult mouse hippocampus (2025) · cites this paper as [71]
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Metadata
- Authors
- Bankhead, P., Loughrey, M. B., Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid, S., Gray, R. T., Murray, L. J., Coleman, H. G., James, J. A., Salto-Tellez, M., & Hamilton, P. W.
- Affiliations
- Queen's University Belfast (GB)
- Corresponding author
- Hamilton, Peter W.
- ORCID
- Venue
- Scientific Reports
- Published
- 2017-12-04
- Volume
- 7
- Issue
- 1
- Pages
- 16878-16878
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5715110
- OpenAlex
- W2952481429
- Cited by
- 8,908
- References
- 37
- Citations by year
- 2026: 1616 · 2025: 1896 · 2024: 1681 · 2023: 1359 · 2022: 1023 · 2021: 704 · 2020: 396 · 2019: 171 · 2018: 49 · 2017: 4
- FWCI
- 99.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- Invest Northern Ireland (RDO0712612)
- Friends of the Cancer Centre
- Cancer Research UK (20256, A15333, C37703/A15333, C11512, A20256, C11512/A20256, C50104/A17592, 15333)
- Public Health Agency (SPI/3315/06, EAT/4905/13)
- Health and Social Care Research and Development Division
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- QuPath, digital pathology, whole slide image analysis, open source software, tumor detection, biomarker analysis, batch processing, scripting, tissue image analysis
- MeSH terms
- Algorithms, Colonic Neoplasms, Humans, Image Interpretation, Computer-Assisted, Biomarkers, Tumor, User-Computer Interface, Kaplan-Meier Estimate, Programmed Cell Death 1 Ligand 2 Protein
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