91³ÉÈ˰涶Òô
Back to

Dr Matthew Field

Lecturer
  • PhD
  • Bachelor of Engineering (Electrical)
Medicine & Health
School of Clinical Medicine

Dr Matthew Field is a researcher at the South Western Sydney Clinical School and the Ingham Institute for Applied Medical Research within the Medical Physics research team. Broadly he supports data science and machine learning projects within a collaborative network of radiation oncology and medical physics departments.

Matthew leads the technical development of a project to connect a network of hospital-based radiotherapy departments (nationally and internationally) to develop machine learning models for various cancer prediction applications.

Professional affiliation:
Institute of Electrical and Electronic Engineers (IEEE) - member
The Trans Tasman Radiation Oncology Group (TROG) - Affiliate member

Journal Review:
Pattern Recognition
Radiotherapy and Oncology
Journal of Medical Imaging and Radiation Oncology
Sensors
Sensor Review
IEEE Robotics and Automation Letters
IEEE Reviews in Biomedical Engineering
Physical and Engineering Sciences in Medicine
Expert Systems with Applications
Artificial Intelligence in Medicine
Phone
+61 2 8738 9220
E-mail
matthew.field@unsw.edu.au
Location
Ingham Institute for Applied Medical Research 1 Campbell Street Liverpool, NSW, 2170
  • Journal articles | 2022
    Field M; Thwaites DI; Carolan M; Delaney GP; Lehmann J; Sykes J; Vinod S; Holloway L, 2022, 'Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer', Journal of Biomedical Informatics, vol. 134, pp. 104181,
    Journal articles | 2022
    Hansen CR; Price G; Field M; Sarup N; Zukauskaite R; Johansen J; Eriksen JG; Aly F; McPartlin A; Holloway L; Thwaites D; Brink C, 2022, 'Open-source distributed learning validation for a larynx cancer survival model following radiotherapy', Radiotherapy and Oncology, vol. 173, pp. 319 - 326,
    Journal articles | 2022
    Kotevski DP; Smee RI; Field M; Nemes YN; Broadley K; Vajdic CM, 2022, 'Evaluation of an automated Presidio anonymisation model for unstructured radiation oncology electronic medical records in an Australian setting', International Journal of Medical Informatics, vol. 168, pp. 104880 - 104880,
    Journal articles | 2022
    Kotevski DP; Smee RI; Vajdic CM; Field M, 2022, 'Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models', Head and Neck,
    Journal articles | 2022
    Rønn Hansen C; Price G; Field M; Sarup N; Zukauskaite R; Johansen J; Eriksen JG; Aly F; McPartlin A; Holloway L; Thwaites D; Brink C, 2022, 'Larynx cancer survival model developed through open-source federated learning', Radiotherapy and Oncology, vol. 176, pp. 179 - 186,
  • Conference Abstracts | 2016
    Jameson MG; Oar AJ; Field M; Ho-Shon I; Phan P; Wang D; Descallar J; Pramana A; Vinod S; Koh E; Holloway LC, 2016, 'Correlation of Gross Tumour Volume and metabolic Tumour Volume for non-small cell lung cancer patients', in RADIOTHERAPY AND ONCOLOGY, ELSEVIER IRELAND LTD, Vol. 118, pp. S51 - S52,
    Conference Abstracts | 2015
    Lehmann J; Bhatia S; Walsh S; Field M; Barakat M; Greer P; Ludbrook J; Dekker A; Holloway L; Vinod S; Thwaites D, 2015, 'Sifting data from the clinical coalface: datamining in radiation oncology to aid clinical decisions', in Asia-Pacific Journal of Clinical Oncology, WILEY-BLACKWELL, Vol. 11, pp. 10 - 10,

ÌýChief investigator for one fellowship grant and co-investigator for 5 grants.

Date

 Investigators Source Title Funds awarded
2018 - 2021 Holloway L., Sowmya A., Dowling J., Vinod S., Field M., Jameson M. UNSW Biomedical seed grant Learning from and Improving target volume delineation in radiotherapy $443,588
2019 - 2022 Field M. Cancer Institute NSW Early Career Researcher Fellowship [ECF181215] Improving lung cancer outcomes with data-driven prognostic imaging biomarkers in a collaborative medical imaging network $420,251
2019 Haidar A., Field M., et al. Ingham Institute Data and Cancer Research Grant Unsupervised Machine Learning for Detecting and Fixing Variations in Cancer Patients Medical Records $15,000
2019 Haidar A., Field M., et al. Ingham Institute Data and Cancer Research Grant Ensemble learning in Cancer Related Applications $25,000
2019 Huang X., Holloway L.,ÌýField M., et al. Ingham Institute Data and Cancer Research Grant Analyzing lung cancer guideline compliance with patient treatment outcomes using deep learning $20,000
2021 - 2023 Holloway L., Field M., et al. Australian Research Data Commons - Platforms Program Australian Cancer Data Network: distributed learning from clinical data $997,000

Ìý

My Research Supervision

Co-supervising twoÌýPhD students at UNSW and two PhD students at University of Wollongong

Ìý