Andrew J. Bulpitt

1.5k total citations
46 papers, 931 citations indexed

About

Andrew J. Bulpitt is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Andrew J. Bulpitt has authored 46 papers receiving a total of 931 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 13 papers in Molecular Biology and 13 papers in Artificial Intelligence. Recurrent topics in Andrew J. Bulpitt's work include Medical Image Segmentation Techniques (9 papers), Machine Learning in Bioinformatics (6 papers) and Surgical Simulation and Training (6 papers). Andrew J. Bulpitt is often cited by papers focused on Medical Image Segmentation Techniques (9 papers), Machine Learning in Bioinformatics (6 papers) and Surgical Simulation and Training (6 papers). Andrew J. Bulpitt collaborates with scholars based in United Kingdom, France and Italy. Andrew J. Bulpitt's co-authors include David R. Westhead, Chris J. Needham, James Bradford, Derek Magee, Neil Sumpter, Darren Treanor, Yi Song, Matthew A. Care, Elizabeth Berry and Ruth K. Wilcox and has published in prestigious journals such as Nature Biotechnology, Bioinformatics and Journal of Molecular Biology.

In The Last Decade

Andrew J. Bulpitt

45 papers receiving 905 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Andrew J. Bulpitt United Kingdom 16 396 225 197 88 86 46 931
Byunghan Lee South Korea 12 791 2.0× 346 1.5× 98 0.5× 128 1.5× 95 1.1× 22 1.6k
Annalisa Barla Italy 19 357 0.9× 151 0.7× 248 1.3× 35 0.4× 65 0.8× 67 1.1k
Wenjie Shu China 21 1.2k 2.9× 150 0.7× 57 0.3× 62 0.7× 109 1.3× 52 1.7k
Fayyaz Minhas United Kingdom 21 507 1.3× 536 2.4× 186 0.9× 67 0.8× 419 4.9× 72 1.4k
Maxwell W. Libbrecht Canada 12 905 2.3× 280 1.2× 47 0.2× 68 0.8× 101 1.2× 29 1.7k
Xiaohong Jia China 16 317 0.8× 318 1.4× 615 3.1× 157 1.8× 306 3.6× 39 1.8k
Guillermo Ayala Spain 17 291 0.7× 71 0.3× 257 1.3× 36 0.4× 132 1.5× 79 1.1k
Tommy Löfstedt Sweden 14 222 0.6× 144 0.6× 103 0.5× 78 0.9× 187 2.2× 35 754
Meng Yang China 16 225 0.6× 265 1.2× 253 1.3× 95 1.1× 189 2.2× 48 1.1k

Countries citing papers authored by Andrew J. Bulpitt

Since Specialization
Citations

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

Fields of papers citing papers by Andrew J. Bulpitt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew J. Bulpitt. 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 Andrew J. Bulpitt. The network helps show where Andrew J. Bulpitt may publish in the future.

Co-authorship network of co-authors of Andrew J. Bulpitt

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew J. Bulpitt. A scholar is included among the top collaborators of Andrew J. Bulpitt 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 Andrew J. Bulpitt. Andrew J. Bulpitt 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.
Bulpitt, Andrew J., et al.. (2024). Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy. Medical Image Analysis. 99. 103379–103379.
2.
Clark, Matthew D., et al.. (2024). Automated extraction of body composition metrics from abdominal CT or MR imaging: A scoping review. European Journal of Radiology. 181. 111764–111764. 3 indexed citations
3.
Thomas, Morgan, Martina Finetti, Steven Pollock, et al.. (2023). PREDICTING GLIOBLASTOMA GENE EXPRESSION THERAPY RESPONSE WITH MACHINE LEARNING. Neuro-Oncology. 25(Supplement_3). iii13–iii14. 1 indexed citations
4.
Ravikumar, Nishant, et al.. (2022). Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2897–2904. 4 indexed citations
5.
Bufano, F., P. Leto, G. Umana, et al.. (2017). First Extended Catalogue of Galactic bubble infrared fluxes from WISE and Herschel★ surveys. Monthly Notices of the Royal Astronomical Society. 473(3). 3671–3692. 2 indexed citations
6.
Seedhom, B B, et al.. (2016). In Vitro Engineering of High Modulus Cartilage-Like Constructs. Tissue Engineering Part C Methods. 22(4). 382–397. 4 indexed citations
7.
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
8.
Ridgway, John P., Eileen Ingham, Darren Treanor, et al.. (2015). A Nondestructive Method to Distinguish the Internal Constituent Architecture of the Intervertebral Discs Using 9.4 Tesla Magnetic Resonance Imaging. Spine. 40(24). E1315–E1322. 4 indexed citations
9.
Villard, Pierre-Frédéric, Franck Vidal, Sheena Johnson, et al.. (2013). Interventional radiology virtual simulator for liver biopsy. International Journal of Computer Assisted Radiology and Surgery. 9(2). 255–267. 21 indexed citations
10.
Song, Yi, Vincent Luboz, Nizar Din, et al.. (2011). Segmentation of 3D Vasculatures for Interventional Radiology Simulation. Studies in health technology and informatics. 163. 599–605. 5 indexed citations
11.
Tedder, Philip, James Bradford, Glenn A. McConkey, Andrew J. Bulpitt, & David R. Westhead. (2010). PlasmoPredict: a gene function prediction website for Plasmodium falciparum. Trends in Parasitology. 26(3). 107–110. 3 indexed citations
12.
Bradford, James, Chris J. Needham, Philip Tedder, et al.. (2009). GO-At :in silicoprediction of gene function inArabidopsis thalianaby combining heterogeneous data. The Plant Journal. 61(4). 713–721. 14 indexed citations
13.
John, Nigel W., Fernando Bello, Chris Hughes, et al.. (2008). Physics-based virtual environment for training core skills in vascular interventional radiological procedures.. PubMed. 132. 195–7. 2 indexed citations
14.
Berry, Elizabeth & Andrew J. Bulpitt. (2008). Fundamentals of MRI. 2 indexed citations
15.
Bulpitt, Andrew J., et al.. (2007). A 6DOF gravity compensation scheme for a phantom premium using a neural network.. PubMed. 125. 43–8. 2 indexed citations
16.
Needham, Chris J., James Bradford, Andrew J. Bulpitt, & David R. Westhead. (2007). A Primer on Learning in Bayesian Networks for Computational Biology. PLoS Computational Biology. 3(8). e129–e129. 214 indexed citations
17.
Needham, Chris J., James Bradford, Andrew J. Bulpitt, Matthew A. Care, & David R. Westhead. (2006). Predicting the effect of missense mutations on protein function: analysis with Bayesian networks. BMC Bioinformatics. 7(1). 405–405. 19 indexed citations
18.
Magee, Derek, Andrew J. Bulpitt, & Elizabeth Berry. (2002). 3D automated segmentation and structural analysis of vascular trees using deformable models. 39. 119–126. 6 indexed citations
19.
Pickering, S. J., et al.. (2001). AI-based algorithms for protein surface comparisons. Computers & Chemistry. 26(1). 79–84. 15 indexed citations
20.
Sumpter, Neil, et al.. (1998). Learning Models of Animal Behaviour for a Robotic Sheepdog. Machine Vision and Applications. 577–580. 2 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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