The DOI matches its Crossref record; 130 of the 130 references it cites were found in a registry or an index.
Digital pathology and computational image analysis in nephropathology
Open accessAbstract
The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.
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“K-nearest neighbors, for instance, is a common used method to construct drug and target similarity networks [171].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [171]
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“Modeling small compounds’ and protein targets’ molecular structure as well as their binding affinities and specificity, for instance, using graph representation learning has enabled accelerated investigation of interactions between a given drug and protein target.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [171]
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Digital pathology and artificial intelligence in translational medicine and clinical practice
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Metadata
- Authors
- Barisoni, L., Lafata, K. J., Hewitt, S. M., Madabhushi, A., & Balis, U. G. J.
- Affiliations
- Duke University (US)
- National Cancer Institute (US)
- Louis Stokes Cleveland VA Medical Center (US)
- Corresponding author
- Barisoni, Laura
- Venue
- Nature Reviews Nephrology
- Published
- 2020-08-26
- Volume
- 16
- Issue
- 11
- Pages
- 669-685
- ISSN
- 1745-8323 (ISSN-L)
- 1759-5061
- 1759-507X
- PMC
- PMC7447970
- OpenAlex
- W3080446999
- Cited by
- 264
- References
- 129
- Citations by year
- 2026: 30 · 2025: 45 · 2024: 57 · 2023: 42 · 2022: 49 · 2021: 38 · 2020: 3
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Reviews Nephrology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Defense (W81XWH-15–1-, W81XWH, W81XWH1510558, W81XWH1810440, W81XWH-18, W81XWH1610329, W81XWH1910668)
- U.S. Department of Veterans Affairs (IBX004121A, I01 BX004121)
- Wallace H. Coulter Foundation
- Nephcure Foundation
- Case Western Reserve University (C06 RR12463)
- Georgia Clinical and Translational Science Alliance
- National Institutes of Health (1r01dk118431-01, W81XWH, DK108805, DK083912)
- National Cancer Institute (U24CA199374, R01CA216579, 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01, U01CA239055, R01CA208236, R01CA202752, R01CA220581, CA220581-01A1)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01‐DK‐118431., U2CDK114886, 1RC2DK01, UH3DK114908, UM1DK100845, DK108805, U54DK083912, U24DK114886, R01DK108805)
- National Institute of Biomedical Imaging and Bioengineering (R43EB028736)
- National Center for Research Resources (C06RR012463, IBX004121A, C06 RR 12463-01)
- DOD Peer Reviewed Cancer Research Program (W81XWH-16–1-0329, W81XWH-18-1-0440)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- digital pathology, nephropathology, renal pathology, computational image analysis, machine vision, machine learning, histopathology image analysis, kidney disease classification, outcome prediction, disease progression risk, targeted therapy, kidney disease prevention
- MeSH terms
- Machine Learning, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Kidney Diseases, Nephrology, Precision Medicine
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