CV

Summary

Electrical engineer and AI researcher with experience spanning federated learning, medical image analysis, radiotherapy data systems, robotics, machine learning, and engineering automation.

Employment

Director, AI Medicine and Engineering (2023-07 to present)
- Consultancy software as a service focusing on automation.
- Research for cancer prediction models, medical imaging.
- Developing software for medical imaging analysis.
- Developing automation solutions for engineering workflows.
Postdoctoral Research Fellow, University of New South Wales (2017-02 to 2023-06)
- Developing federated learning systems for cancer prediction - AusCAT and ACDN.
- Collaborative research with medical physics, oncologists and researchers.
- Supervising research students.
Postdoctoral Research Fellow, University of Wollongong (2016-01 to 2017-02)
- Developing federated learning systems for cancer prediction - AusCAT and ACDN.
- Collaborative research with medical physics, oncologists and researchers.
- Supervising research students.
Postdoctoral Research Fellow, University of Sydney (2014-09 to 2016-01)
- Developing federated learning systems for cancer prediction - AusCAT.
- Collaborative research with medical physics, oncologists and researchers.

Education

PhD Artificial Intelligence, University of Wollongong (2015)
- Thesis title: “Acquisition and distribution of synergistic reactive control skills”. Supervised by Prof David Stirling.
- Read Thesis
BEng Electrical Engineering, University of Wollongong (2006)
- Graduated with First Class Honours

Research interests

Artificial Intelligence
Medical Image Analysis
Deep Learning
Robotics
Machine Learning

Technical skills

Technical Skills
- Python
- Data Science
- SQL

Awards

Early Career Researcher Fellowship — Cancer Institute NSW (2019)
- Awarded for research on federated learning and radiomics for cancer prediction models.
Travel Grant — Cancer Institute NSW (2022)
- Awarded to support research on cancer prediction models.

Publications

Development of federated learning neural networks with combined horizontal and vertical data partitioning

Amir Anees, Matthew Field, Lois Holloway (2026). "Development of federated learning neural networks with combined horizontal and vertical data partitioning." Applied Soft Computing. 114734.

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

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.

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

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.

Radiotherapy protocol compliance in routine clinical practice for patients with stages I–III non-small-cell lung cancer

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.

Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non–small Cell Lung Cancer Using Real-World Data

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.

A neural network-based vertical federated learning framework with server integration

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.

Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Iromi R. Paranavithana, David Stirling, Montserrat Ros, Matthew Field (2023). "Systematic Review of Tumor Segmentation Strategies for Bone Metastases." Cancers. 15(6).

Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach

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).

Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer–Specific Survival Using Electronic Health Record Data: A Multisite Study

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.

Inter-hospital variation in data collection, radiotherapy treatment, and survival in patients with head and neck cancer: A multisite study

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.

Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models

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.

Training radiomics-based CNNs for clinical outcome prediction: Challenges, strategies and findings

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.

Optimal and actual rates of Stereotactic Ablative Body Radiotherapy (SABR) utilisation for primary lung cancer in Australia

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.

Larynx cancer survival model developed through open-source federated learning

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.

Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer

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.

Evaluation of an automated Presidio anonymisation model for unstructured radiation oncology electronic medical records in an Australian setting

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.

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

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.

PSPSO: A package for parameters selection using particle swarm optimization

Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway (2021). "PSPSO: A package for parameters selection using particle swarm optimization." SoftwareX. 15, 100706.

Machine learning applications in radiation oncology

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.

Implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning

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.

Deep learning for segmentation in radiation therapy planning: a review

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.

Multicenter evaluation of MRI-based radiomic features: A phantom study

Robba Rai, Lois C. Holloway, Carsten Brink, Matthew Field, Rasmus L. Christiansen, Yu Sun, Michael B. Barton, Gary P. Liney (2020). "Multicenter evaluation of MRI-based radiomic features: A phantom study." Medical Physics. 47(7), 3054-3063.

The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review

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).

A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy

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.

Matthew Field

CV