Lucy M. Warren

597 total citations
25 papers, 319 citations indexed

About

Lucy M. Warren is a scholar working on Pulmonary and Respiratory Medicine, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Lucy M. Warren has authored 25 papers receiving a total of 319 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Pulmonary and Respiratory Medicine, 19 papers in Artificial Intelligence and 18 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Lucy M. Warren's work include Digital Radiography and Breast Imaging (21 papers), AI in cancer detection (19 papers) and Medical Imaging Techniques and Applications (11 papers). Lucy M. Warren is often cited by papers focused on Digital Radiography and Breast Imaging (21 papers), AI in cancer detection (19 papers) and Medical Imaging Techniques and Applications (11 papers). Lucy M. Warren collaborates with scholars based in United Kingdom, United States and Belgium. Lucy M. Warren's co-authors include Kenneth C. Young, David R. Dance, Alistair Mackenzie, Rosalind Given-Wilson, Matthew Wallis, Julie Cooke, Dev P. Chakraborty, Hilde Bosmans, Mark Halling‐Brown and Premkumar Elangovan and has published in prestigious journals such as British Journal of Cancer, American Journal of Roentgenology and Physics in Medicine and Biology.

In The Last Decade

Lucy M. Warren

25 papers receiving 313 citations

Peers

Lucy M. Warren
Bruno Barufaldi United States
Olivier Alonzo‐Proulx Canada
Bjørn Helge Østerås Norway
William R. Geiser United States
Raymond J. Acciavatti United States
Murilo Falleiros Lemos Schmitt Brazil
Simon John Christoph Soerensen United States
Junfeng Xiong China
Ramona W. Bouwman Netherlands
Jingchen Ma China
Bruno Barufaldi United States
Lucy M. Warren
Citations per year, relative to Lucy M. Warren Lucy M. Warren (= 1×) peers Bruno Barufaldi

Countries citing papers authored by Lucy M. Warren

Since Specialization
Citations

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

Fields of papers citing papers by Lucy M. Warren

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lucy M. Warren. 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 Lucy M. Warren. The network helps show where Lucy M. Warren may publish in the future.

Co-authorship network of co-authors of Lucy M. Warren

This figure shows the co-authorship network connecting the top 25 collaborators of Lucy M. Warren. A scholar is included among the top collaborators of Lucy M. Warren 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 Lucy M. Warren. Lucy M. Warren 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.
Ellis, Sam, Matthew Trumble, Mark Halling‐Brown, et al.. (2024). Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort. Radiology Artificial Intelligence. 6(4). e230431–e230431. 4 indexed citations
2.
Young, Kenneth C., Mark Halling‐Brown, Stephen W. Duffy, et al.. (2023). Lessons learned from independent external validation of an AI tool to detect breast cancer using a representative UK data set. British Journal of Radiology. 96(1143). 20211104–20211104. 3 indexed citations
3.
Burnside, Elizabeth S., Lucy M. Warren, Jonathan P. Myles, et al.. (2021). Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case–control study. British Journal of Cancer. 125(6). 884–892. 6 indexed citations
4.
Mackenzie, Alistair, Melissa P. Mitchell, Premkumar Elangovan, et al.. (2021). Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial. Medical Physics. 48(11). 6859–6868. 6 indexed citations
5.
Mackenzie, Alistair, Melissa P. Mitchell, Premkumar Elangovan, et al.. (2021). Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging. European Radiology. 32(2). 806–814. 9 indexed citations
6.
Warren, Lucy M., Peter C. Harris, Sandra Terezinha Marques Gomes, et al.. (2020). Deep learning to calculate breast density from processed mammography images. 24–24. 2 indexed citations
7.
Elangovan, Premkumar, Alistair Mackenzie, Lucy M. Warren, et al.. (2019). Validation of modelling tools for simulating wide-angle DBT systems. 85–85. 2 indexed citations
8.
Warren, Lucy M., et al.. (2019). Is there a safety-net effect with computer-aided detection (CAD)?. Research Explorer (The University of Manchester). 23. 18–18. 1 indexed citations
9.
Warren, Lucy M., Mark Halling‐Brown, Louise Wilkinson, et al.. (2019). Changes in breast density. 11. 32–32. 1 indexed citations
10.
Warren, Lucy M., Mark Halling‐Brown, David R. Dance, et al.. (2017). Image processing can cause some malignant soft-tissue lesions to be missed in digital mammography images. Clinical Radiology. 72(9). 799.e1–799.e8. 4 indexed citations
11.
Warren, Lucy M., et al.. (2016). VALIDATION OF A SIMULATION PROCEDURE FOR GENERATING BREAST TOMOSYNTHESIS PROJECTION IMAGES. Radiation Protection Dosimetry. 169(1-4). 386–391. 1 indexed citations
12.
Mackenzie, Alistair, Lucy M. Warren, Matthew Wallis, et al.. (2016). The relationship between cancer detection in mammography and image quality measurements. Physica Medica. 32(4). 568–574. 35 indexed citations
13.
Warren, Lucy M., David R. Dance, & Kenneth C. Young. (2016). Radiation risk of breast screening in England with digital mammography. British Journal of Radiology. 89(1067). 20150897–20150897. 27 indexed citations
14.
Mackenzie, Alistair, Lucy M. Warren, Matthew Wallis, et al.. (2015). Breast cancer detection rates using four different types of mammography detectors. European Radiology. 26(3). 874–883. 16 indexed citations
15.
Halling‐Brown, Mark, et al.. (2014). The oncology medical image database (OMI-DB). Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9039. 903906–903906. 14 indexed citations
16.
Elangovan, Premkumar, Lucy M. Warren, Alistair Mackenzie, et al.. (2014). Development and validation of a modelling framework for simulating 2D-mammography and breast tomosynthesis images. Physics in Medicine and Biology. 59(15). 4275–4293. 41 indexed citations
17.
Warren, Lucy M., et al.. (2013). Validation of simulation of calcifications for observer studies in digital mammography. Physics in Medicine and Biology. 58(16). N217–N228. 13 indexed citations
18.
Warren, Lucy M., Alistair Mackenzie, David R. Dance, & Kenneth C. Young. (2013). Comparison of the x-ray attenuation properties of breast calcifications, aluminium, hydroxyapatite and calcium oxalate. Physics in Medicine and Biology. 58(7). N103–N113. 31 indexed citations
19.
Warren, Lucy M., Alistair Mackenzie, Julie Cooke, et al.. (2012). Effect of image quality on calcification detection in digital mammography. Medical Physics. 39(6Part1). 3202–3213. 67 indexed citations
20.
Warren, Lucy M., Alistair Mackenzie, Julie Cooke, et al.. (2012). Mammographic calcification cluster detection and threshold gold thickness measurements. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8313. 83130J–83130J. 1 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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