Research output per year
Research output per year
Personal profile
Biography
Jake has completed a Bachelor of Science (majoring in Engineering Science and Pharmacology), a Diploma of Physics, a Masters of Physics (Medical Physics), and a PhD in medical physics.
He has a strong interest in the utilisation of artificial intelligence (AI) and other computational techniques for the analysis of medical images in order to enhance a precision medicine approach to patient management. His research spans the use of AI for the automated detection of metastatic prostate cancer, radiomics for survival prognosis, and the derivation of imaging biomarkers with prognostic significance from molecular imaging modalities.
Jake is a current lead and founding member of the UWA Machine Learning in Cancer Research group which provides students from a variety of fields with a forum to discuss their machine learning-related projects. This group was awarded the distinguished Award for Programs that Enhance Learning by the School of Physics, Mathematics and Computing in October of 2022. Jake is also a member of the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) AI Working Group, tasked with preparing the college for the expected professional changes resulting from the clinical implementation of AI-based solutions.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Research expertise keywords
- Prostate Cancer
- PSMA PET/CT
- Artificial intelligence
- Quantitative Imaging
- Machine Learning
- Medical Physics
- Nuclear Medicine
- Diagnostic Imaging
- Radiomics
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Assessing the Heterogeneity of Response of [68Ga] Ga-PSMA-11 PET/CT Lesions in Patients With Biochemical Recurrence of Prostate Cancer
Dell'Oro, M., Huff, D. T., Lokre, O., Kendrick, J., Govindan, R. M., Ong, J. S. L., Ebert, M. A., Perk, T. G. & Francis, R. J., Oct 2024, In: Clinical Genitourinary Cancer. 22, 5, 102155.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus) -
From inception to the present: A review analysing the application of the radiomics quality score.
Barry, N., Kendrick, J., Rowshan Farzad, P., Hassan, M. & Ebert, M., 2024.Research output: Contribution to conference › Poster › peer-review
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Tomo-NeRL: tomographic neural representation learning for implicit CT image reconstruction
Tang, X., Tan, B., Guo, Y., Yu, X., Kendrick, J. & Reynolds, M., Dec 2024, 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Cannataro, M., Zheng, H., Gao, L., Cheng, J., de Miranda, J. L., Zumpano, E., Hu, X., Cho, Y.-R. & Park, T. (eds.). IEEE DataPort, p. 2481-2488 8 p. 10822003Research output: Chapter in Book/Conference paper › Conference paper › peer-review
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Delineation and agreement of FET PET biological volumes in glioblastoma: results of the nuclear medicine credentialing program from the prospective, multi-centre trial evaluating FET PET In Glioblastoma (FIG) study—TROG 18.06
Barry, N., Francis, R. J., Ebert, M. A., Koh, E. S., Rowshanfarzad, P., Hassan, G. M., Kendrick, J., Gan, H. K., Lee, S. T., Lau, E., Moffat, B. A., Fitt, G., Moore, A., Thomas, P., Pattison, D. A., Akhurst, T., Alipour, R., Thomas, E. L., Hsiao, E. & Schembri, G. P. & 18 others, , Nov 2023, In: European Journal of Nuclear Medicine and Molecular Imaging. 50, 13, p. 3970-3981 12 p.Research output: Contribution to journal › Article › peer-review
Open Access9 Citations (Scopus) -
Prognostic utility of RECIP 1.0 with manual and AI-based segmentations in biochemically recurrent prostate cancer from [68Ga]Ga-PSMA-11 PET images
Kendrick, J., Francis, R. J., Hassan, G. M., Rowshan Farzad, P., Ong, J. S., McCarthy, M. C., Alexander, S. & Ebert, M., Nov 2023, In: European Journal of Nuclear Medicine and Molecular Imaging. 50, 13, p. 4077-4086 10 p.Research output: Contribution to journal › Article › peer-review
Open Access5 Citations (Scopus)
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Clinical Evaluation of Deep Learning for Identification and Prognosis in Metastatic Prostate Cancer
Kendrick, J. (Investigator 01), Ebert, M. (Investigator 02), Hassan, M. (Investigator 03), Barry, N. (Investigator 04), Rowshan Farzad, P. (Investigator 05), Rusanov, B. (Investigator 06), Ong, J. (Investigator 07) & Dell'Oro, M. (Investigator 08)
Department of Health (Western Australia)
1/01/25 → 1/01/27
Project: Research
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Conversion between brain cancer imaging modalities using generative AI
Ebert, M. (Investigator 01), Barry, N. (Investigator 02), Rusanov, B. (Investigator 03), Kendrick, J. (Investigator 04), Safdar, S. (Investigator 05), Rowshan Farzad, P. (Investigator 06) & Hassan, M. (Investigator 07)
Department of Health (Western Australia)
13/10/23 → 31/12/24
Project: Research
Prizes
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PMC Citation for Outstanding Contributions to Student Learning (Team)
Kendrick, J. (Recipient) & Barry, N. (Recipient), 17 Dec 2024
Prize: Award
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PMC Commendation for Outstanding Contributions to Student Learning
Kendrick, J. (Recipient), 17 Dec 2024
Prize: Award
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Activities
- 1 Membership of professional association
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Australasian College of Physical Scientists & Engineers in Medicine - AI Working Group (External organisation)
Kendrick, J. (Member)
1 Jul 2021Activity: Memberships › Membership of professional association
Press/Media
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Revolutionary applications of AI empower researchers to create real-world impact
23/07/24
1 item of Media coverage
Press/Media: Press / Media