Matthew Field

1.1k total citations
43 papers, 756 citations indexed

About

Matthew Field is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Matthew Field has authored 43 papers receiving a total of 756 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Radiology, Nuclear Medicine and Imaging, 19 papers in Artificial Intelligence and 18 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Matthew Field's work include Radiomics and Machine Learning in Medical Imaging (21 papers), Lung Cancer Diagnosis and Treatment (15 papers) and AI in cancer detection (11 papers). Matthew Field is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (21 papers), Lung Cancer Diagnosis and Treatment (15 papers) and AI in cancer detection (11 papers). Matthew Field collaborates with scholars based in Australia, United States and Netherlands. Matthew Field's co-authors include David Stirling, Lois Holloway, Fazel Naghdy, Zengxi Pan, Montserrat Ros, Martin Carolan, Michael Jameson, Andrew Miller, Shalini Vinod and Christian Ritz and has published in prestigious journals such as Pattern Recognition, Medical Physics and IEEE Transactions on Cybernetics.

In The Last Decade

Matthew Field

42 papers receiving 735 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew Field Australia 14 310 192 154 141 95 43 756
Shan Yang Switzerland 12 466 1.5× 151 0.8× 227 1.5× 125 0.9× 331 3.5× 43 1.1k
Yongwon Cho South Korea 17 465 1.5× 160 0.8× 172 1.1× 163 1.2× 125 1.3× 59 1.0k
Michael Friebe Germany 17 316 1.0× 177 0.9× 290 1.9× 223 1.6× 92 1.0× 146 1.1k
Wenjun Tan China 18 366 1.2× 149 0.8× 173 1.1× 172 1.2× 207 2.2× 94 996
Richard E. Fan United States 22 522 1.7× 217 1.1× 407 2.6× 662 4.7× 142 1.5× 79 1.5k
Hélder P. Oliveira Portugal 20 304 1.0× 251 1.3× 110 0.7× 207 1.5× 173 1.8× 94 916
Constantinos Loukas Greece 20 208 0.7× 208 1.1× 304 2.0× 105 0.7× 353 3.7× 61 1.1k
Lia Morra Italy 15 307 1.0× 282 1.5× 75 0.5× 99 0.7× 159 1.7× 49 749
Stamatia Giannarou United Kingdom 18 104 0.3× 67 0.3× 377 2.4× 69 0.5× 380 4.0× 56 926
Germán González United States 18 317 1.0× 96 0.5× 129 0.8× 204 1.4× 181 1.9× 56 913

Countries citing papers authored by Matthew Field

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Field

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Field

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Field. A scholar is included among the top collaborators of Matthew Field 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 Matthew Field. Matthew Field 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.
Huang, Xiaoshui, Matthew Field, Shalini Vinod, et al.. (2024). Radiotherapy protocol compliance in routine clinical practice for patients with stages I–III non‐small‐cell lung cancer. Journal of Medical Imaging and Radiation Oncology. 68(6). 729–739. 1 indexed citations
2.
Field, Matthew, Shalini Vinod, Geoff P. Delaney, et al.. (2024). Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non–small Cell Lung Cancer Using Real-World Data. Clinical Oncology. 36(7). e197–e208. 4 indexed citations
3.
Anees, Amir, Matthew Field, & Lois Holloway. (2024). A neural network-based vertical federated learning framework with server integration. Engineering Applications of Artificial Intelligence. 138. 109276–109276. 2 indexed citations
4.
Chlap, Phillip, Hang Min, Jason Dowling, et al.. (2024). Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets. Computerized Medical Imaging and Graphics. 116. 102403–102403. 4 indexed citations
5.
Field, Matthew, et al.. (2023). Automated detection, delineation and quantification of whole-body bone metastasis using FDG-PET/CT images. Physical and Engineering Sciences in Medicine. 46(2). 851–863. 4 indexed citations
6.
Haidar, Ali, Matthew Field, Vikneswary Batumalai, et al.. (2023). Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach. Cancers. 15(3). 564–564. 4 indexed citations
7.
Stirling, David, et al.. (2023). Systematic Review of Tumor Segmentation Strategies for Bone Metastases. Cancers. 15(6). 1750–1750. 9 indexed citations
8.
Smee, Robert I., et al.. (2023). The Utility of Oncology Information Systems for Prognostic Modelling in Head and Neck Cancer. Journal of Medical Systems. 47(1). 9–9. 1 indexed citations
9.
Field, Matthew, David Thwaites, Martin Carolan, et al.. (2022). Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer. Journal of Biomedical Informatics. 134. 104181–104181. 13 indexed citations
10.
11.
Holloway, Lois, et al.. (2022). Optimal and actual rates of Stereotactic Ablative Body Radiotherapy (SABR) utilisation for primary lung cancer in Australia. Clinical and Translational Radiation Oncology. 34. 7–14. 3 indexed citations
12.
Hansen, Christian Rønn, Gareth Price, Matthew Field, et al.. (2022). Larynx cancer survival model developed through open-source federated learning. Radiotherapy and Oncology. 176. 179–186. 26 indexed citations
13.
Smee, Robert I., et al.. (2022). Evaluation of an automated Presidio anonymisation model for unstructured radiation oncology electronic medical records in an Australian setting. International Journal of Medical Informatics. 168. 104880–104880. 7 indexed citations
14.
Hansen, Christian Rønn, Gareth Price, Matthew Field, et al.. (2022). Open-source distributed learning validation for a larynx cancer survival model following radiotherapy. Radiotherapy and Oncology. 173. 319–326. 7 indexed citations
15.
Field, Matthew, Shalini Vinod, Noel J. Aherne, et al.. (2021). Implementation of the Australian Computer‐Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Journal of Medical Imaging and Radiation Oncology. 65(5). 627–636. 16 indexed citations
16.
Field, Matthew, Nicholas Hardcastle, Michael Jameson, Noel J. Aherne, & Lois Holloway. (2021). Machine learning applications in radiation oncology. Physics and Imaging in Radiation Oncology. 19. 13–24. 55 indexed citations
17.
Pang, Shuchao, Matthew Field, Jason Dowling, et al.. (2021). Training radiomics-based CNNs for clinical outcome prediction: Challenges, strategies and findings. Artificial Intelligence in Medicine. 123. 102230–102230. 10 indexed citations
18.
Haidar, Ali, Matthew Field, Jonathan Sykes, Martin Carolan, & Lois Holloway. (2021). PSPSO: A package for parameters selection using particle swarm optimization. SoftwareX. 15. 100706–100706. 17 indexed citations
19.
Vial, Alanna, David Stirling, Matthew Field, et al.. (2018). The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Translational Cancer Research. 7(3). 803–816. 121 indexed citations
20.
Field, Matthew, Aditya Ghose, David Stirling, et al.. (2017). The effect of imputing missing clinical attribute values on training lung cancer survival prediction model performance. Health Information Science and Systems. 5(1). 16–16. 13 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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