Dr Joshua Astley

Clinical Medicine, School of Medicine and Population Health

Postdoctoral Research Associate

Dr Joshua Astley
Dr Joshua Astley
Profile picture of Dr Joshua Astley
j.r.astley@sheffield.ac.uk

Full contact details

Dr Joshua Astley
Clinical Medicine, School of Medicine and Population Health
Polaris
18 Claremont Crescent
Sheffield
S10 2TA
Profile

I obtained a degree in Mechanical Engineering from the University of Sheffield in 2019 before securing a fully-funded Faculty of Medicine Post Graduate Research Committee Scholarship to complete a PhD within the POLARIS group at the University of Sheffield. I completed my PhD in 2023 and in recognition of my PhD thesis, I received the prestigious Institute of Physics Best PhD Thesis in Medical Physics award. I am currently working as a Postdoctoral Research Associate in Pulmonary MR Image Computing Science at the University of Sheffield.

Research interests

My research interests focus on lung image analysis methods, primarily in MR imaging, alongside biomarker prediction in patients with lung disease. I have published several peer reviewed research articles and conference abstracts on the use of neural networks in medical imaging applications, such as image segmentation, synthesis and survival analysis, with the aim of improving patient care. I have a keen interest in the scientific method and producing research that is rigorously validated and statistically sound. I am excited by the developing fields of explainable AI, multi-modal fusion models, uncertainty awareness and AI ethics.

Publications

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Journal articles

Conference proceedings papers

All publications

Journal articles

Conference proceedings papers

Current projects
  • Longitudinal prediction of lung biomarkers via a diverse range of clinical, demographic and imaging data derived from a large cohort of patients with asthma and/or COPD acquired as part of a sub-study of the NOVELTY study https://noveltystudy.com/
  • A CNN-based end-to-end approach for image-based biomarker prediction from 129Xe-MRI and 1H-MRI with the aim of improving functional lung clinical workflows.
  • Use of explainable AI techniques, such as grad-CAM and SHAP, to provide improved trust in algorithms amongst clinicians and patients.
  • Investigating the use of fusion models for multi-modal prediction and survival analysis in lung cancer radiotherapy.