Joseph R. Ledsam

11.5k total citations · 2 hit papers
14 papers, 1.9k citations indexed

About

Joseph R. Ledsam is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Hepatology. According to data from OpenAlex, Joseph R. Ledsam has authored 14 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Health Informatics and 4 papers in Hepatology. Recurrent topics in Joseph R. Ledsam's work include MRI in cancer diagnosis (5 papers), Hepatocellular Carcinoma Treatment and Prognosis (4 papers) and Artificial Intelligence in Healthcare and Education (4 papers). Joseph R. Ledsam is often cited by papers focused on MRI in cancer diagnosis (5 papers), Hepatocellular Carcinoma Treatment and Prognosis (4 papers) and Artificial Intelligence in Healthcare and Education (4 papers). Joseph R. Ledsam collaborates with scholars based in United Kingdom, United States and Japan. Joseph R. Ledsam's co-authors include Xiaoxuan Liu, Gabriella Moraes, Pearse A. Keane, Lucas M. Bachmann, Siegfried K. Wagner, Konstantinos Balaskas, Alastair K. Denniston, Dun Jack Fu, Livia Faes and Martin Schmid and has published in prestigious journals such as Nature Methods, Investigative Ophthalmology & Visual Science and Radiotherapy and Oncology.

In The Last Decade

Joseph R. Ledsam

14 papers receiving 1.9k citations

Hit Papers

A comparison of deep learning performance against health-... 2019 2026 2021 2023 2019 2021 250 500 750 1000
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.

  1. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
    2019 1.0k citations Xiaoxuan Liu, Livia Faes et al. The Lancet Digital Health profile →
  2. Effective gene expression prediction from sequence by integrating long-range interactions
    2021 486 citations Žiga Avsec, Vikram Agarwal et al. Nature Methods profile →

Peers

Joseph R. Ledsam
Marcus A. Badgeley United States
Lisa C. Adams Germany
Yiftach Barash Israel
Dun Jack Fu United Kingdom
Junjie Bai China
Saeed Hassanpour United States
Oishi Banerjee United States
Keno K. Bressem Germany
Bryan He United States
Konstantinos Exarchos Greece
Marcus A. Badgeley United States
Joseph R. Ledsam
Citations per year, relative to Joseph R. Ledsam Joseph R. Ledsam (= 1×) peers Marcus A. Badgeley

Countries citing papers authored by Joseph R. Ledsam

Since Specialization
Citations

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

Fields of papers citing papers by Joseph R. Ledsam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joseph R. Ledsam. 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 Joseph R. Ledsam. The network helps show where Joseph R. Ledsam may publish in the future.

Co-authorship network of co-authors of Joseph R. Ledsam

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

All Works

14 of 14 papers shown
1.
Miyagi, Yumi, Hiroki Kayama, Shawn Xu, et al.. (2024). Artificial intelligence as a second reader for screening mammography. 1(2). 1 indexed citations
2.
Avsec, Žiga, Vikram Agarwal, Daniel Visentin, et al.. (2021). Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods. 18(10). 1196–1203. 486 indexed citations breakdown →
3.
Chopra, Reena, Gabriella Moraes, Dun Jack Fu, et al.. (2020). Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence. Investigative Ophthalmology & Visual Science. 61(7). 1152–1152. 1 indexed citations
4.
5.
Kosmin, Michael, Joseph R. Ledsam, Bernardino Romera‐Paredes, et al.. (2019). Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer. Radiotherapy and Oncology. 135. 130–140. 99 indexed citations
6.
Wagner, Siegfried K., Reena Chopra, Joseph R. Ledsam, et al.. (2019). Diagnostic accuracy and interobserver variability of macular disease evaluation using optical coherence tomography. Investigative Ophthalmology & Visual Science. 60(9). 1849–1849. 2 indexed citations
7.
Faes, Livia, Siegfried K. Wagner, Dun Jack Fu, et al.. (2019). Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. The Lancet Digital Health. 1(5). e232–e242. 201 indexed citations
8.
Liu, Xiaoxuan, Livia Faes, Aditya U. Kale, et al.. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health. 1(6). e271–e297. 1020 indexed citations breakdown →
9.
Faes, Livia, Xiaoxuan Liu, Aditya U. Kale, et al.. (2019). Deep Learning Under Scrutiny: Performance Against Health Care Professionals in Detecting Diseases from Medical Imaging - Systematic Review and Meta-Analysis. SSRN Electronic Journal. 14 indexed citations
10.
Faes, Livia, Siegfried K. Wagner, Dun Jack Fu, et al.. (2019). Feasibility of Automated Deep Learning Design for Medical Image Classification by Healthcare Professionals with Limited Coding Experience. SSRN Electronic Journal. 2 indexed citations
11.
Saito, Kazuhiro, Joseph R. Ledsam, Katsutoshi Sugimoto, et al.. (2018). DCE-MRI for Early Prediction of Response in Hepatocellular Carcinoma after TACE and Sorafenib Therapy: A Pilot Study. Journal of the Belgian Society of Radiology. 102(1). 40–40. 11 indexed citations
12.
13.
Ledsam, Joseph R., Richard Hodgson, Robert J. Moots, & Steven Sourbron. (2013). Modeling DCE‐MRI at low temporal resolution: A case study on rheumatoid arthritis. Journal of Magnetic Resonance Imaging. 38(6). 1554–1563. 7 indexed citations
14.
Saito, Kazuhiro, et al.. (2012). Assessing liver function using dynamic Gd‐EOB‐DTPA‐enhanced MRI with a standard 5‐phase imaging protocol. Journal of Magnetic Resonance Imaging. 37(5). 1109–1114. 51 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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