Darren Treanor

7.2k total citations · 2 hit papers
145 papers, 3.8k citations indexed

About

Darren Treanor is a scholar working on Artificial Intelligence, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Darren Treanor has authored 145 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 87 papers in Artificial Intelligence, 36 papers in Oncology and 34 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Darren Treanor's work include AI in cancer detection (87 papers), Cell Image Analysis Techniques (31 papers) and Radiomics and Machine Learning in Medical Imaging (26 papers). Darren Treanor is often cited by papers focused on AI in cancer detection (87 papers), Cell Image Analysis Techniques (31 papers) and Radiomics and Machine Learning in Medical Imaging (26 papers). Darren Treanor collaborates with scholars based in United Kingdom, Sweden and United States. Darren Treanor's co-authors include Derek Magee, Bethany Williams, Philip Quirke, Nasir Rajpoot, Adnan Mujahid Khan, Jon Griffin, Rebecca Randell, Kieran Sheahan, Emily L. Clarke and Hugh Mulcahy and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Gastroenterology.

In The Last Decade

Darren Treanor

140 papers receiving 3.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution

2014 384 citations Darren Treanor, Derek Magee et al. profile →
Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy

2024 67 citations Clare McGenity, Emily L. Clarke et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Darren Treanor United Kingdom	30	1.7k	1.2k	1.0k	673	664	145	3.8k
Yukako Yagi United States	34	1.7k 1.0×	524 0.4×	990 1.0×	367 0.5×	572 0.9×	127	3.6k
Gabriele Campanella United States	21	1.2k 0.7×	912 0.7×	1.0k 1.0×	204 0.3×	453 0.7×	31	3.9k
John Tomaszewski United States	33	2.1k 1.2×	379 0.3×	1.5k 1.5×	322 0.5×	1.2k 1.8×	79	3.9k
Alexander T. Pearson United States	32	1.2k 0.7×	1.3k 1.1×	1.2k 1.2×	458 0.7×	206 0.3×	159	4.1k
Peter Bult Netherlands	36	1.2k 0.7×	1.3k 1.1×	1.6k 1.6×	767 1.1×	375 0.6×	120	5.3k
Hannah Gilmore United States	33	2.3k 1.3×	1.4k 1.1×	2.3k 2.3×	211 0.3×	1.0k 1.5×	78	5.4k
Jakob Nikolas Kather Germany	44	3.3k 1.9×	2.1k 1.7×	3.3k 3.3×	449 0.7×	709 1.1×	214	7.7k
Natalie Shih United States	24	1.4k 0.8×	494 0.4×	1.1k 1.1×	136 0.2×	750 1.1×	47	2.8k
Titus J. Brinker Germany	28	1.4k 0.8×	1.4k 1.1×	857 0.8×	135 0.2×	245 0.4×	105	3.0k
Jason Hipp United States	24	1.2k 0.7×	455 0.4×	849 0.8×	200 0.3×	323 0.5×	58	2.9k

Countries citing papers authored by Darren Treanor

Since Specialization

Citations

This map shows the geographic impact of Darren Treanor'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 Darren Treanor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Darren Treanor more than expected).

Fields of papers citing papers by Darren Treanor

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Darren Treanor. 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 Darren Treanor. The network helps show where Darren Treanor may publish in the future.

Co-authorship network of co-authors of Darren Treanor

This figure shows the co-authorship network connecting the top 25 collaborators of Darren Treanor. A scholar is included among the top collaborators of Darren Treanor 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 Darren Treanor. Darren Treanor is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Brettle, D S, et al.. (2025). An international study of stain variability in histopathology using qualitative and quantitative analysis. Journal of Pathology Informatics. 17. 100423–100423. 5 indexed citations

McGenity, Clare, et al.. (2025). Liver-Quant: Feature-based image analysis toolkit for automatic quantification of metabolic dysfunction-associated steatotic liver disease. Computers in Biology and Medicine. 190. 110049–110049. 3 indexed citations

Pye, Hayley, et al.. (2025). Use of quality checks and processes across digital histopathology: an initial survey from the Bigpicture consortium. Journal of Clinical Pathology. 78(10). 691–696.

Wright, Alexander, et al.. (2024). Object-based feedback attention in convolutional neural networks improves tumour detection in digital pathology. Scientific Reports. 14(1). 30400–30400.

McGenity, Clare, et al.. (2024). Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. npj Digital Medicine. 7(1). 114–114. 67 indexed citations breakdown →

Nsengimana, Jérémie, Graham P. Cook, Darren Treanor, et al.. (2024). Immune subtyping of melanoma whole slide images using multiple instance learning. Medical Image Analysis. 93. 103097–103097. 15 indexed citations

Humphries, Matthew P., et al.. (2024). Development of a multi‐scanner facility for data acquisition for digital pathology artificial intelligence. The Journal of Pathology. 264(1). 80–89. 2 indexed citations

Clarke, Emily L., Derek Magee, Julia Newton‐Bishop, et al.. (2024). The Development and Evaluation of a Convolutional Neural Network for Cutaneous Melanoma Detection in Whole Slide Images. Archives of Pathology & Laboratory Medicine. 149(9). 831–837.

Rowe, Ian, et al.. (2024). WED-254 The true diagnostic performance of magnetic resonance elastography for the detection of advanced fibrosis in metabolic dysfunction-associated steatotic liver disease exceeds the diagnostic performance of liver biopsy. Journal of Hepatology. 80. S530–S530.

10.

Bándi, Péter, et al.. (2022). Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection. Cancers. 14(21). 5424–5424. 19 indexed citations

11.

Wright, Judy, et al.. (2022). What Works Where and How for Uptake and Impact of Artificial Intelligence in Pathology: Review of Theories for a Realist Evaluation. Journal of Medical Internet Research. 25. e38039–e38039. 7 indexed citations

12.

Molin, Jesper, et al.. (2021). The human‐in‐the‐loop: an evaluation of pathologists’ interaction with artificial intelligence in clinical practice. Histopathology. 79(2). 210–218. 20 indexed citations

13.

Wright, Alexander, et al.. (2020). The Effect of Quality Control on Accuracy of Digital Pathology Image Analysis. IEEE Journal of Biomedical and Health Informatics. 25(2). 307–314. 36 indexed citations

14.

Hedlund, Joel, et al.. (2019). Axillary lymph nodes in breast cancer cases. 5 indexed citations

15.

Skoglund, Karin, et al.. (2019). Colon data from the Visual Sweden project DROID. 1 indexed citations

16.

Dessauvagie, Benjamin F., Andrew H.S. Lee, Katie Meehan, et al.. (2018). Interobserver variation in the diagnosis of fibroepithelial lesions of the breast: a multicentre audit by digital pathology. Journal of Clinical Pathology. 71(8). 672–679. 19 indexed citations

17.

Williams, Bethany, Jessica Lee, Karin A. Oien, & Darren Treanor. (2018). Digital pathology access and usage in the UK: results from a national survey on behalf of the National Cancer Research Institute’s CM-Path initiative. Journal of Clinical Pathology. 71(5). 463–466. 49 indexed citations

18.

Randell, Rebecca, Roy A. Ruddle, & Darren Treanor. (2015). Barriers and facilitators to the introduction of digital pathology for diagnostic work.. PubMed. 216. 443–7. 9 indexed citations

19.

Bulpitt, Andrew J., et al.. (2015). A Novel Approach for the Colour Deconvolution of Multiple Histological Stains.. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 156–162. 7 indexed citations

20.

Magee, Derek, et al.. (2011). Context-Based Classification of Cell Nuclei and Tissue Regions in Liver Histopathology.. 239–244. 5 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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