Matthew Field

Matthew Field

Director of AI Medicine and Engineering. Electrical engineer and AI researcher working across federated learning, medical image analysis, robotics, and healthcare data systems.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer

Graphical abstract

Graphical abstract for the study on federated learning-based prognostic models for anal cancer.

Published in Nature Communications, 2026

Nature Communications. Topics: Distributed learning, Federated learning, Neural networks.

Recommended citation: Stelios Theophanous, Per-Ivar Lønne, Ananya Choudhury, Maaike Berbee, Charlotte Deijen, Andre Dekker, Matthew Field, Maria Antonietta Gambacorta, Alexandra Gilbert, Marianne Grønlie Guren, Rashmi Jadon, Rohit Kochhar, Daniel Martin, Ahmed Allam Mohamed, Rebecca Muirhead, Oriol Parés, Łukasz Raszewski, Rajarshi Roy, Andrew Scarsbrook, David Sebag-Montefiore, Emiliano Spezi, Vassilios Vassiliou, Eirik Malinen, Leonard Wee, atomCAT Consortium (2026). "An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer." Nature Communications. 114734.
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Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets

Graphical abstract

Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets.

Published in Computerized Medical Imaging and Graphics, 2024

Computerized Medical Imaging and Graphics. Topics: Auto-segmentation, Deep learning, Uncertainty estimation.

Recommended citation: Phillip Chlap, Hang Min, Jason Dowling, Matthew Field, Kirrily Cloak, Trevor Leong, Mark Lee, Julie Chu, Jennifer Tan, Phillip Tran, Tomas Kron, Mark Sidhom, Kirsty Wiltshire, Sarah Keats, Andrew Kneebone, Annette Haworth, Martin A. Ebert, Shalini K. Vinod, Lois Holloway (2024). "Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets." Computerized Medical Imaging and Graphics. 116, 102403.
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Radiotherapy protocol compliance in routine clinical practice for patients with stages I–III non-small-cell lung cancer

Published in Journal of Medical Imaging and Radiation Oncology, 2024

Journal of Medical Imaging and Radiation Oncology. Topics: compliance, guideline, lung neoplasms.

Recommended citation: Xiaoshui Huang, Matthew Field, Shalini Vinod, Helen Ball, Vikneswary Batumalai, Paul Keall, Lois Holloway (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.
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Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non–small Cell Lung Cancer Using Real-World Data

Graphical abstract

Graphical abstract showing a seven-clinic federated learning workflow for stage I-III non-small cell lung cancer radiotherapy, with 1655 eligible patients, model AUC 0.68, and a projected 11% increase in 2-year survival.

Published in Clinical Oncology, 2024

Clinical Oncology. Topics: Decision support, federated learning, lung cancer.

Recommended citation: M. Field, S. Vinod, G. P. Delaney, N. Aherne, M. Bailey, M. Carolan, A. Dekker, S. Greenham, E. Hau, J. Lehmann, J. Ludbrook, A. Miller, A. Rezo, J. Selvaraj, J. Sykes, D. Thwaites, L. Holloway (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.
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A neural network-based vertical federated learning framework with server integration

Graphical abstract

Vertical federated learning with server integration enables privacy-preserving collaborative neural-network training across split datasets while maintaining centralized-level performance.

Published in Engineering Applications of Artificial Intelligence, 2024

Engineering Applications of Artificial Intelligence. Topics: Federated learning, Vertical partitioned data, Accuracy.

Recommended citation: Amir Anees, Matthew Field, Lois Holloway (2024). "A neural network-based vertical federated learning framework with server integration." Engineering Applications of Artificial Intelligence. 138, 109276.
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Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach

Published in Cancers, 2023

Cancers. DOI available.

Recommended citation: Ali Haidar, Matthew Field, Vikneswary Batumalai, Kirrily Cloak, Daniel Al Mouiee, Phillip Chlap, Xiaoshui Huang, Vicky Chin, Farhannah Aly, Martin Carolan, Jonathan Sykes, Shalini K. Vinod, Geoffrey P. Delaney, Lois Holloway (2023). "Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach." Cancers. 15(3).
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Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer–Specific Survival Using Electronic Health Record Data: A Multisite Study

Graphical abstract

Development and validation of machine learning models and a nomogram to predict 2-year cancer-specific survival in a large multisite head and neck cancer cohort using electronic health record data.

Published in JCO Clinical Cancer Informatics, 2023

JCO Clinical Cancer Informatics. DOI available.

Recommended citation: Damian P. Kotevski, Robert I. Smee, Claire M. Vajdic, Matthew Field (2023). "Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer–Specific Survival Using Electronic Health Record Data: A Multisite Study." JCO Clinical Cancer Informatics. e2200128.
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Inter-hospital variation in data collection, radiotherapy treatment, and survival in patients with head and neck cancer: A multisite study

Published in Radiotherapy and Oncology, 2023

Radiotherapy and Oncology. Topics: Head and neck cancer, Inter-hospital variation, Survival.

Recommended citation: Damian P. Kotevski, Claire M. Vajdic, Matthew Field, Robert I. Smee (2023). "Inter-hospital variation in data collection, radiotherapy treatment, and survival in patients with head and neck cancer: A multisite study." Radiotherapy and Oncology. 188, 109843.
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Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models

Published in Head & Neck, 2023

Head & Neck. Topics: cancer-specific survival, head and neck cancer, machine learning.

Recommended citation: Damian P. Kotevski, Robert I. Smee, Claire M. Vajdic, Matthew Field (2023). "Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models." Head & Neck. 45(2), 365-379.
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Training radiomics-based CNNs for clinical outcome prediction: Challenges, strategies and findings

Published in Artificial Intelligence in Medicine, 2022

Artificial Intelligence in Medicine. Topics: Cancer outcome prediction, Head & neck cancers, Deep neural networks.

Recommended citation: Shuchao Pang, Matthew Field, Jason Dowling, Shalini Vinod, Lois Holloway, Arcot Sowmya (2022). "Training radiomics-based CNNs for clinical outcome prediction: Challenges, strategies and findings." Artificial Intelligence in Medicine. 123, 102230.
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Optimal and actual rates of Stereotactic Ablative Body Radiotherapy (SABR) utilisation for primary lung cancer in Australia

Published in Clinical and Translational Radiation Oncology, 2022

Clinical and Translational Radiation Oncology. Topics: Lung SABR, Optimal utilisation, Practice patterns.

Recommended citation: Wsam Ghandourh, Lois Holloway, Vikneswary Batumalai, Phillip Chlap, Matthew Field, Susannah Jacob (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.
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Larynx cancer survival model developed through open-source federated learning

Graphical abstract

Overview of a federated learning approach used for collaborative development of a larynx cancer survival prediction model using harmonized data from international hospitals while preserving privacy.

Published in Radiotherapy and Oncology, 2022

Radiotherapy and Oncology. Topics: Distributed learning, Federated learning, Larynx cancer.

Recommended citation: Christian Rønn Hansen, Gareth Price, Matthew Field, Nis Sarup, Ruta Zukauskaite, Jørgen Johansen, Jesper Grau Eriksen, Farhannah Aly, Andrew McPartlin, Lois Holloway, David Thwaites, Carsten Brink (2022). "Larynx cancer survival model developed through open-source federated learning." Radiotherapy and Oncology. 176, 179-186.
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Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer

Graphical abstract

Conceptual overview of the proposed distributed machine learning infrastructure for computer-assisted cancer theragnostics, showing secure multi-site data harmonization, privacy-preserving collaborative training, and development of predictive models for treatment-related clinical events.

Published in Journal of Biomedical Informatics, 2022

Journal of Biomedical Informatics. Topics: Data mining, Decision support systems, Distributed learning.

Recommended citation: Matthew Field, David I. Thwaites, Martin Carolan, Geoff P. Delaney, Joerg Lehmann, Jonathan Sykes, Shalini Vinod, Lois Holloway (2022). "Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer." Journal of Biomedical Informatics. 134, 104181.
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Evaluation of an automated Presidio anonymisation model for unstructured radiation oncology electronic medical records in an Australian setting

Published in International Journal of Medical Informatics, 2022

International Journal of Medical Informatics. Topics: Electronic medical records, Oncology information systems, Personally identifiable information.

Recommended citation: Damian P. Kotevski, Robert I. Smee, Matthew Field, Yvonne N. Nemes, Kathryn Broadley, Claire M. Vajdic (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.
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Machine learning applications in radiation oncology

Graphical abstract

Graphical abstract summarizing machine learning applications in radiation oncology across decision support, segmentation, treatment planning, and image-guided treatment, supported by clinical, imaging, dose, and outcome data.

Published in Physics and Imaging in Radiation Oncology, 2021

Physics and Imaging in Radiation Oncology. Topics: Machine learning, Artificial intelligence, Radiation therapy.

Recommended citation: Matthew Field, Nicholas Hardcastle, Michael Jameson, Noel Aherne, Lois Holloway (2021). "Machine learning applications in radiation oncology." Physics and Imaging in Radiation Oncology. 19, 13-24.
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Implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning

Graphical abstract

Overview of the implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for secure, harmonized data extraction, standardization, analysis and federated learning in radiation oncology to support multi-institutional precision oncology research while preserving data privacy.

Published in Journal of Medical Imaging and Radiation Oncology, 2021

Journal of Medical Imaging and Radiation Oncology. Topics: artificial intelligence, decision support systems, distributed learning.

Recommended citation: Matthew Field, Shalini Vinod, Noel Aherne, Martin Carolan, Andre Dekker, Geoff Delaney, Stuart Greenham, Eric Hau, Joerg Lehmann, Joanna Ludbrook, Andrew Miller, Angela Rezo, Jothybasu Selvaraj, Jonathan Sykes, Lois Holloway, David Thwaites (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.
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Deep learning for segmentation in radiation therapy planning: a review

Published in Journal of Medical Imaging and Radiation Oncology, 2021

Journal of Medical Imaging and Radiation Oncology. Topics: contouring, deep learning, radiation therapy.

Recommended citation: Gihan Samarasinghe, Michael Jameson, Shalini Vinod, Matthew Field, Jason Dowling, Arcot Sowmya, Lois Holloway (2021). "Deep learning for segmentation in radiation therapy planning: a review." Journal of Medical Imaging and Radiation Oncology. 65(5), 578-595.
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The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review

Published in Translational Cancer Research, 2018

Translational Cancer Research. Topics: .

Recommended citation: Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Holloway, Alexis A. Miller (2018). "The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review." Translational Cancer Research. 7(3).
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A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy

Published in Acta Oncologica, 2018

Acta Oncologica. DOI available.

Recommended citation: Arthur Jochems, Issam El-Naqa, Marc Kessler, Charles S. Mayo, Shruti Jolly, Martha Matuszak, Corinne Faivre-Finn, Gareth Price, Lois Holloway, Shalini Vinod, Matthew Field, Mohamed Samir Barakat, David Thwaites, Dirk de Ruysscher, Andre Dekker, Philippe Lambin (2018). "A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy." Acta Oncologica. 57(2), 226--230.
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Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers

Published in IEEE Transactions on Cybernetics, 2016

IEEE Transactions on Cybernetics. Topics: Hidden Markov models, Robot kinematics, Trajectory.

Recommended citation: Matthew Field, David Stirling, Zengxi Pan, Fazel Naghdy (2016). "Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers." IEEE Transactions on Cybernetics. 46(3), 706-717.
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Conference Papers

An Improved Deep Learning Framework for MR-to-CT Image Synthesis with a New Hybrid Objective Function

Published in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). Topics: Deep learning, Training, Visualization.

Recommended citation: Sui Paul Ang, Son Lam Phung, Matthew Field, Mark Matthias Schira (2022). "An Improved Deep Learning Framework for MR-to-CT Image Synthesis with a New Hybrid Objective Function." 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). 1-5.
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Inertial sensing for human motor control symmetry in injury rehabilitation

Published in 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Topics: Sensors.

Recommended citation: Matthew Field, David Stirling, Montserrat Ros, Zengxi Pan, Fazel Naghdy (2013). "Inertial sensing for human motor control symmetry in injury rehabilitation." 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. 1470-1475.
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Motion capture in robotics review

Published in 2009 IEEE International Conference on Control and Automation, 2009

2009 IEEE International Conference on Control and Automation. Topics: Robot sensing systems, Tracking, Intelligent robots.

Recommended citation: Matthew Field, David Stirling, Fazel Naghdy, Zengxi Pan (2009). "Motion capture in robotics review." 2009 IEEE International Conference on Control and Automation. 1697-1702.
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