David Clunie

2.2k total citations
44 papers, 838 citations indexed

About

David Clunie is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, David Clunie has authored 44 papers receiving a total of 838 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Radiology, Nuclear Medicine and Imaging, 18 papers in Artificial Intelligence and 11 papers in Pulmonary and Respiratory Medicine. Recurrent topics in David Clunie's work include Radiomics and Machine Learning in Medical Imaging (17 papers), AI in cancer detection (17 papers) and Medical Imaging Techniques and Applications (11 papers). David Clunie is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (17 papers), AI in cancer detection (17 papers) and Medical Imaging Techniques and Applications (11 papers). David Clunie collaborates with scholars based in United States, Germany and United Kingdom. David Clunie's co-authors include Joseph H. Piatt, Andriy Fedorov, Ron Kikinis, John Freymann, Justin Kirby, John Perry, C. Carl Jaffe, Steve Pieper, Simon Roman-Goldstein and Liam J Caffery and has published in prestigious journals such as Nature Communications, Cancer Research and Radiology.

In The Last Decade

David Clunie

41 papers receiving 809 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Clunie United States 19 351 244 209 156 104 44 838
Máté E. Maros Germany 12 266 0.8× 195 0.8× 127 0.6× 80 0.5× 155 1.5× 43 906
Carson Lam United States 13 606 1.7× 275 1.1× 106 0.5× 179 1.1× 55 0.5× 25 1.1k
Jihye Yun South Korea 15 564 1.6× 190 0.8× 323 1.5× 110 0.7× 137 1.3× 36 985
Raphael Meier Switzerland 16 455 1.3× 254 1.0× 114 0.5× 219 1.4× 106 1.0× 36 1.0k
Si‐Wa Chan Taiwan 15 530 1.5× 369 1.5× 296 1.4× 80 0.5× 78 0.8× 44 879
Miguel Souto Spain 17 455 1.3× 435 1.8× 328 1.6× 430 2.8× 109 1.0× 58 1.0k
Albert Comelli Italy 23 710 2.0× 197 0.8× 382 1.8× 168 1.1× 265 2.5× 67 1.2k
Fausto Milletarì United States 12 304 0.9× 393 1.6× 80 0.4× 262 1.7× 115 1.1× 21 892
Hidetaka Arimura Japan 19 819 2.3× 245 1.0× 547 2.6× 185 1.2× 223 2.1× 123 1.3k
Gopichandh Danala United States 11 447 1.3× 342 1.4× 123 0.6× 88 0.6× 51 0.5× 35 654

Countries citing papers authored by David Clunie

Since Specialization
Citations

This map shows the geographic impact of David Clunie's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Clunie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Clunie more than expected).

Fields of papers citing papers by David Clunie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Clunie. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Clunie. The network helps show where David Clunie may publish in the future.

Co-authorship network of co-authors of David Clunie

This figure shows the co-authorship network connecting the top 25 collaborators of David Clunie. A scholar is included among the top collaborators of David Clunie based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Clunie. David Clunie is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Philbrick, Kenneth A., et al.. (2025). Maintaining and broadening DICOM adoption in digital pathology: A response to “wearing a fur coat in the summertime”. Journal of Pathology Informatics. 19. 100517–100517.
2.
Fischer, Maximilian, Peter Neher, Peter J. Schüffler, et al.. (2025). Unlocking the potential of digital pathology: Novel baselines for compression. Journal of Pathology Informatics. 17. 100421–100421.
3.
Murugesan, Gowtham Krishnan, Mariam Aboian, Keyvan Farahani, et al.. (2024). AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons. Scientific Data. 11(1). 1165–1165. 2 indexed citations
4.
Bontempi, Dennis, et al.. (2024). Enrichment of lung cancer computed tomography collections with AI-derived annotations. Scientific Data. 11(1). 25–25. 2 indexed citations
5.
Roth, Christopher J., Cheryl A. Petersilge, David Clunie, et al.. (2024). HIMSS-SIIM Enterprise Imaging Community White Papers: Reflections and Future Directions. Journal of Imaging Informatics in Medicine. 37(2). 429–443.
6.
Pieper, Steven, William J.R. Longabaugh, David Clunie, et al.. (2023). Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology. Nature Communications. 14(1). 1572–1572. 19 indexed citations
7.
Herrmann, Markus D., David Clunie, William Kingdon Clifford, et al.. (2023). The NCI Imaging Data Commons as a platform for reproducible research in computational pathology. Computer Methods and Programs in Biomedicine. 242. 107839–107839. 5 indexed citations
8.
Bridge, Christopher P., Steven C. Pieper, Jochen K. Lennerz, et al.. (2022). Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology. Journal of Digital Imaging. 35(6). 1719–1737. 9 indexed citations
9.
Kalen, Joseph D., David Clunie, Yanling Liu, et al.. (2021). Design and Implementation of the Pre-Clinical DICOM Standard in Multi-Cohort Murine Studies. Tomography. 7(1). 1–9. 2 indexed citations
10.
Caffery, Liam J, Veronica Rotemberg, Jochen Weber, et al.. (2021). The Role of DICOM in Artificial Intelligence for Skin Disease. Frontiers in Medicine. 7. 619787–619787. 18 indexed citations
11.
Towbin, Alexander J., et al.. (2021). The Importance of Body Part Labeling to Enable Enterprise Imaging: A HIMSS-SIIM Enterprise Imaging Community Collaborative White Paper. Journal of Digital Imaging. 34(1). 1–15. 15 indexed citations
12.
Fedorov, Andriy, David Clunie, Mathias Brochhausen, et al.. (2020). DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules. Medical Physics. 47(11). 5953–5965. 11 indexed citations
13.
Clunie, David. (2019). Dual-Personality DICOM-TIFF for Whole Slide Images: A Migration Technique for Legacy Software. Journal of Pathology Informatics. 10(1). 12–12. 13 indexed citations
14.
Fedorov, Andriy, Michael Schwier, David Clunie, et al.. (2018). An annotated test-retest collection of prostate multiparametric MRI. Scientific Data. 5(1). 180281–180281. 26 indexed citations
15.
Fillion‐Robin, Jean‐Christophe, Michael D. Onken, Jörg Riesmeier, et al.. (2017). dcmqi : An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. Cancer Research. 77(21). e87–e90. 28 indexed citations
16.
17.
McNitt‐Gray, Michael F., Grace Hyun J. Kim, Binsheng Zhao, et al.. (2015). Determining the Variability of Lesion Size Measurements from CT Patient Data Sets Acquired under “No Change” Conditions. Translational Oncology. 8(1). 55–64. 21 indexed citations
18.
Pierce, Larry A., et al.. (2015). A Digital Reference Object to Analyze Calculation Accuracy of PET Standardized Uptake Value. Radiology. 277(2). 538–545. 25 indexed citations
19.
Clunie, David. (2007). DICOM Structured Reporting and Cancer Clinical Trials Results. Cancer Informatics. 4. CIN.S37032–CIN.S37032. 44 indexed citations
20.
Heinz, Grant W., David Clunie, & Paul B. Mullaney. (1998). The effect of buphthalmos on orbital growth in early childhood: Increased orbital soft tissue volume strongly correlates with increased orbital volume. Journal of American Association for Pediatric Ophthalmology and Strabismus. 2(1). 39–42. 13 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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