Invited speakers

Dr Daniel McGowan

Head of Education and Research, Consultant Clinical Scientist

Oxford University Hospitals NHS Foundation Trust

Daniel is the Head of Education and Research for the Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT where he is a Medical Physics Expert and Radioactive Waste Advisor.  He is an Honorary Senior Clinical Research Fellow with the University of Oxford’s Department of Oncology and is Course Director for their MSc in Medical Physics with Radiobiology.  He supervises a number of doctoral students within his Clinical Academic Group.  He is a Fellow of the British Institute of Radiology, Institute of Physics and Engineering in Medicine, Higher Education Academy and the Academy of Healthcare Science.  He is an Honorary Senior Research Fellow in the School of Medicine, Cardiff University.  He has been a member of the British Institute of Radiology Nuclear Medicine and Molecular Imaging Committee since 2013, chair since 2021.  He was Co-Chair of the UK Internal Dosimetry Users Group (IDUG) 2011-2013 and has served as Emeritus Chair since 2014. 

Professor Ben Glocker

Ben Glocker is Professor in Machine Learning for Imaging at the Department of Computing at Imperial College London where he co-leads the Biomedical Image Analysis Group. He is the Kheiron Medical Technologies / Royal Academy of Engineering Research Chair in Safe Deployment of Medical Imaging AI. He also leads the HeartFlow-Imperial Research Team and is Head of ML Research at Kheiron.

He is also the Knowledge Transfer Lead of the EPSRC Causality in Healthcare AI Hub. His research is at the intersection of medical imaging and artificial intelligence aiming to build safe and ethical computational tools for improving image-based detection and diagnosis of disease.

Mr Richard Meades

Principal Nuclear Medicine Physicist

The Royal Free London NHS Foundation Trust

As a state registered clinical scientist in the NHS, Richard leads the provision of medical physics expertise for one of the largest radionuclide therapy services in the NHS. He has over 20 years of clinical and research experience in diagnostic and therapeutic nuclear medicine, radiation protection, national and international clinical trials, staff training and has held several honorary academic lecturing positions.

Active in the development and implementation of Artificial Intelligence (AI) in healthcare, Richard has published peer review publications and professional magazine articles in this area, founded and chairs the Institute of Physics and Engineering in Medicine's (IPEM) AI group, is a member of the European Federation of Medical Physics (EFOMP) AI Group Steering Committee and the AXREM AI Special Focus Group. He also works on the strategic and collaborative AI initiatives of IPEM through his senior volunteering roles as a member of its Science, Technology, Engineering, Research and Innovation Council (STERIC) and President’s Advisory Committee. In addition, he has been an invited conference speaker and lectures on AI and has sat on national conference Scientific Organising Committees/Steering Groups to advise on and curate their AI content.

Ms Camarie Welgemoed

Ms Camarie Welgemoed

Research and Development Lead Radiographer at Imperial College Healthcare NHS Trust

Camarie is a dual-qualified radiographer with more than 30 years of work experience in radiotherapy. She started a breast specialist role in 2003, followed by a part-time MSC in 2005. Her research project comprised the evaluation of the then-current field-based nodal technique. At the time, there were no guidelines or expertise in nodal contouring.

In collaboration with Dominique Blunt, a radiologist, they developed nodal contouring guidelines, demonstrated that nodal volumes were under-dosed in 60% of patients, and presented at numerous conferences. The time-consuming nature of manual contouring did not go unnoticed in the radiotherapy community. Mirada Medical supplied her with a research licence to start her part-time PhD research in ABAS at Imperial College London in 2017. She recently passed her viva with minor amendments.

She works as a Research and Development Lead Radiographer in Radiotherapy at Imperial College Healthcare NHS Trust (Charing Cross Hospital) and hopes to continue her research in predictive modelling in breast radiotherapy.

In her spare time, she enjoys golf, camping, and walking her dog.
Today, she will present her research on auto-segmentation and Radiomics in breast Radiotherapy.

Dr Matthew Blackledge

Dr. Blackledge is a dedicated researcher specializing in the intersection of magnetic resonance imaging (MRI) and artificial intelligence (AI) to advance cancer prognosis. During his PhD, Dr. Blackledge developed the "computed diffusion-weighted MRI" technique a method now implemented in clinical MRI scanners globally. In his post-doctoral work, he created automated methods for detecting advanced bone cancers in whole-body MRI, which have since been commercialized as a certified medical device.

Currently, Dr. Blackledge leads the Computational Imaging Group at the Institute of Cancer Research (ICR) in the United Kingdom. The group focuses on enhancing AI techniques for cancer diagnostics, with applications across advanced prostate, breast, and lung cancers, gynecological diseases, sarcoma, and multiple myeloma. The group’s research leverages MRI and CT datasets to improve treatment response evaluation and radiotherapy planning.

Committed to translating research into clinical practice, Dr. Blackledge collaborates closely with medical device manufacturers, develops custom computational tools for existing medical imaging platforms, and has co-founded a spin-out company in partnership with the ICR.

Dr. Blackledge's current research agenda includes:

1. Establishing Bayesian techniques to improve personalized cancer treatment, including investigations into how tumor heterogeneity, identified through multiparametric imaging, impacts therapy response and informs adaptive treatment strategies.
2. Advancing AI-driven imaging and image analysis to lower the costs associated with quantitative MRI, particularly whole-body imaging, benefiting both patients and healthcare providers. This work includes automated quality assessment of AI algorithms to facilitate clinical acceptance and is aimed at establishing models for personalized treatment in advanced cancers and population screening applications.
3. Exploring new quantitative imaging methods, such as magnetic resonance elastography, whole-body intravoxel incoherent motion modeling, and contrast clearance analysis, to assess cancers with heterogeneous response patterns both within and across tumors.
4. Ensuring that all methodologies are deployable and sharable for research use by other teams, fostering broader collaboration and impact.