Cambridge MedAI Seminar - April 2025

Add to your list(s) Download to your calendar using vCal

• Daniel Kreuter and Dr Ander Biguri
• Friday 25 April 2025, 11:45-13:00
• Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge.

If you have a question about this talk, please contact Hannah Clayton.

Sign up on Eventbrite: https://medai_april2025.eventbrite.co.uk

Join us for the Cambridge AI in Medicine Seminar Series, hosted by the Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke’s. This series brings together leading experts to explore cutting-edge AI applications in healthcare—from disease diagnosis to drug discovery. It’s a unique opportunity for researchers, practitioners, and students to stay at the forefront of AI innovations and engage in discussions shaping the future of AI in healthcare.

This month’s seminar will be held on Friday 25 April 2025, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom. A light lunch from Aromi will be served from 11:45. The event will feature the following talks:

Unlocking Hidden Potential: Federated Machine Learning on Blood Count Data Enables Accurate Iron Deficiency Detection in Blood Donors – Daniel Kreuter, PhD Student, Department of Applied Mathematics and Theoretical Physics, University of Cambridge

Daniel is a PhD student in the BloodCounts! project, focusing on building algorithms for advanced full blood count analysis to extract additional clinical information from the world’s most common medical test. His research aims to improve healthcare decision-making through more efficient use of existing data. He is in his final year and is supervised by Prof Carola-Bibiane Schönlieb from the Applied Mathematics department and Prof Willem Ouwehand from the department of Haematology. Before coming to Cambridge, Daniel studied physics at the Technische Universität Darmstadt in Germany. His Master’s thesis project focused on replacing costly laser-plasma interaction simulations with a much faster neural network model, reducing computation time from 4 hours to a few milliseconds.

Abstract: The full blood count is the world’s most common medical laboratory test, with 3.6 billion tests performed annually worldwide. Despite this ubiquity, the rich single-cell flow cytometry data generated by haematology analysers to calculate standard parameters like haemoglobin and cell counts is routinely discarded. Our research demonstrates how AI models can extract this hidden value, transforming a routine test into a powerful screening tool for iron deficiency in blood donors—with no additional testing required. Iron deficiency remains a major challenge in blood donation programs, affecting donor health and donation efficiency. By applying advanced machine learning to previously unused data dimensions within standard blood counts, we achieve significantly improved detection accuracy compared to conventional parameters. Furthermore, we show that federated learning enables this approach to scale and generalise across multiple centres while preserving data privacy. This work exemplifies how AI can enhance existing medical infrastructure, extracting new clinical value from already-collected data to improve donor health.

Reconstructing extremely low dose CT images using machine learning – Dr Ander Biguri, Senior Research Associate, Department of Applied Mathematics and Theoretical Physics, University of Cambridge

Ander Biguri received his Ph.D. in Electrical Engineering from the University of Bath in 2018, for his work on 4D Computed Tomography for radiotherapy. Since, he has held research positions at University of Southampton, University College London and lastly University of Cambridge. His research lies in the intersection of inverse problems and their applications in real-case scenarios, such as Positron Emission Tomography or various computed tomography modalities. He is best known for the development of the TIGRE toolbox for applied tomography applications.

Abstract: ML models can be used to denoise medical images, however when doing this we don’t use information from the measurements. You can instead add machine learning to the image formation/reconstruction process, ensuring high quality images that still match the measured data from medical scanners. In this talk we will briefly see different ways of adding machine learning to these mathematical processes and discuss the challenges still needed to be tackled to make the application of such methods a clinical reality.

This is a hybrid event so you can also join via Zoom:

https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09

Meeting ID: 990 5046 7573 and Passcode: 617729

We look forward to your participation! If you are interested in getting involved and presenting your work, please email Ines Machado at im549@cam.ac.uk

For more information about this seminar series, see: https://www.integratedcancermedicine.org/research/cambridge-medai-seminar-series/

This talk is part of the Cambridge MedAI Seminar Series series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• CCC talks for website
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge MedAI Seminar Series
• Cambridge talks
• Chris Davis' list
• Interested Talks
• Interested Talks
• Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.