L. Escudero

6.5k total citations
8 papers, 38 citations indexed

About

L. Escudero is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, L. Escudero has authored 8 papers receiving a total of 38 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Radiology, Nuclear Medicine and Imaging, 3 papers in Biomedical Engineering and 2 papers in Artificial Intelligence. Recurrent topics in L. Escudero's work include Radiomics and Machine Learning in Medical Imaging (4 papers), Advanced X-ray and CT Imaging (3 papers) and Particle physics theoretical and experimental studies (2 papers). L. Escudero is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (4 papers), Advanced X-ray and CT Imaging (3 papers) and Particle physics theoretical and experimental studies (2 papers). L. Escudero collaborates with scholars based in United Kingdom, Austria and Portugal. L. Escudero's co-authors include Mireia Crispin‐Ortuzar, Cathal McCague, Evis Sala, James D. Brenton, Ramona Woitek, Ozan Öktem, Leonardo Rundo, Erik Ziegler, Mohammad Al Sa’d and James A. d’Arcy and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and BMC Medicine.

In The Last Decade

L. Escudero

7 papers receiving 38 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
L. Escudero United Kingdom	3	20	17	9	7	4	8	38
Peter M. Full Germany	4	9 0.5×	24 1.4×	5 0.6×	5 0.7×	2 0.5×	6	35
Tim Rädsch Germany	2	16 0.8×	24 1.4×	9 1.0×	5 0.7×		3	46
John M. Pfeifer United States	4	35 1.8×	9 0.5×	7 0.8×	8 1.1×	1 0.3×	6	65
Jayashree Kalpathy-Cramer United States	4	15 0.8×	15 0.9×	5 0.6×	2 0.3×	14 3.5×	10	41
Sanmi Koyejo United States	4	10 0.5×	28 1.6×	7 0.8×	2 0.3×		14	48
Alan Karthikesalingam United Kingdom	3	20 1.0×	32 1.9×	24 2.7×	5 0.7×		5	70
Daniel Ting Singapore	3	49 2.5×	7 0.4×	10 1.1×	3 0.4×		4	80
Carla Pitarch Spain	2	10 0.5×	5 0.3×	4 0.4×	3 0.4×	5 1.3×	4	14
Jorge Corral Acero United Kingdom	3	23 1.1×	7 0.4×	3 0.3×	6 0.9×		5	50
Tingyao Li China	3	42 2.1×	14 0.8×	7 0.8×		6 1.5×	4	66

Countries citing papers authored by L. Escudero

Since Specialization

Citations

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

Fields of papers citing papers by L. Escudero

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of L. Escudero

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Chen, Jian, Yancheng Xu, Guangming Wang, et al.. (2025). Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography. BMC Medicine. 23(1). 657–657.

2.

Manavaki, Roido, et al.. (2024). Mitigating the impact of image processing variations on tumour [18F]-FDG-PET radiomic feature robustness. Scientific Reports. 14(1). 16294–16294. 1 indexed citations

3.

Escudero, L., Mohammad Al Sa’d, Cathal McCague, et al.. (2023). Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case. Diagnostics. 13(17). 2813–2813. 2 indexed citations

4.

Escudero, L., Mireia Crispin‐Ortuzar, Ramona Woitek, et al.. (2023). Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation. Computers in Biology and Medicine. 163. 107096–107096. 20 indexed citations

5.

Escudero, L., Cathal McCague, Grant D. Stewart, et al.. (2022). Artificial intelligence for early detection of renal cancer in computed tomography: A review. SHILAP Revista de lepidopterología. 1. e4–e4. 3 indexed citations

6.

Doran, Simon, Mohammad Al Sa’d, James A. d’Arcy, et al.. (2022). Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies. Tomography. 8(1). 497–512. 10 indexed citations

7.

Marshall, J., A. Blake, M. Thomson, et al.. (2017). The Pandora multi-algorithm approach to automated pattern recognition in LAr TPC detectors. Journal of Physics Conference Series. 888. 12142–12142. 1 indexed citations

8.

Escudero, L.. (2016). Initial Probe of δ by the T2K Experiment with ν Disappearance and ν Appearance. Nuclear and Particle Physics Proceedings. 273-275. 1814–1819. 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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