Cambridge MedAI Seminar - November 2025

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• Rui Li and Rehan Zuberi
• Wednesday 26 November 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.

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 Wednesday 26 November 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:

LUMEN – A deep learning pipeline for analysis of the 3D morphology of the cerebral lenticulostriate arteries from time-of-flight 7T MRI – Rui Li, PhD student, Department of Clinical Neurosciences, University of Cambridge

Rui Li is a PhD student at the Stroke Research Group, led by Professor Hugh Markus, in the Department of Clinical Neurosciences, University of Cambridge. Her doctoral research focuses on applying machine learning to neuroimage analysis in cerebral small vessel disease research. Specifically, her work involves developing methods for the segmentation and quantification of the morphology of small cerebral perforating arteries from 7T MRI , and applying machine learning to dementia prediction in cerebral small vessel disease from multimodal MRI . Prior to her PhD, she studied information engineering and bioengineering in the Department of Engineering at Cambridge.

Abstract: The lenticulostriate arteries (LSAs) supply critical subcortical brain structures and are affected in cerebral small vessel disease (CSVD). Changes in their morphology are linked to cardiovascular risk factors and may indicate early pathology. 7T Time-of-Flight MR angiography (TOF-MRA) enables clear LSA visualisation. We aimed to develop a semi-automated pipeline for quantifying 3D LSA morphology from 7T TOF -MRA in CSVD patients.

We used data from a local 7T CSVD study to create a pipeline, LUMEN , comprising two stages: vessel segmentation and LSA quantification. For segmentation, we fine-tuned a deep learning model, DS6 , and compared it against nnU-Net and a Frangi-filter pipeline, MSFDF . For quantification, centrelines of LSAs within basal ganglia were extracted to compute branch counts, length, tortuosity, and maximum curvature. This pipeline was applied to 69 subjects, with results compared to traditional analysis measuring LSA morphology on 2D coronal maximum intensity projection (MIP) images.

For vessel segmentation, fine-tuned DS6 achieved the highest test Dice score (0.814±0.029) and sensitivity, whereas nnU-Net achieved the best balanced average Hausdorff distance and precision. Visual inspection confirmed that DS6 was most sensitive in detecting LSAs with weak signals. Across 69 subjects, the pipeline with DS6 identified 23.5 ± 8.5 LSA branches. Branch length inside the basal ganglia was 26.4 ± 3.5 mm, and tortuosity was 1.5 ± 0.1. Extracted LSA metrics from 2D MIP analysis and our 3D analysis showed fair-to-moderate correlations. Outliers highlighted the added value of 3D analysis.

This open-source deep-learning-based pipeline offers a validated tool quantifying 3D LSA morphology in CSVD patients from 7T-TOF-MRA for clinical research.

Multimodal Learning to Predict Progression in Barrett’s Oesophagus – Rehan Zuberi, PhD student, Cancer Research UK Cambridge Institute, University of Cambridge

Rehan Zuberi is a PhD researcher at the Cancer Research UK Cambridge Institute in the Markowetz Lab. His work focuses on developing machine learning architectures and applying them to cancer research, with an emphasis on building clinically relevant multimodal models for early detection of disease.

Abstract: Oesophageal cancer is one of the deadliest cancers, with most patients not surviving beyond five years. Early detection is therefore critical, but current surveillance methods often miss subtle progression signals. This talk will present a multimodal deep learning framework that integrates whole slide histopathology features with genomic copy number variation (CNV) data to predict progression in Barrett’s oesophagus. We use weakly supervised multiple instance learning for image representation and an intermediate fusion architecture to combine modalities. I will discuss dataset composition, fusion strategies, and early performance benchmarks, as well as the unique signal captured beyond unimodal baselines. I will also outline future work exploring additional modalities such as clinical and longitudinal data. This approach aims to improve risk stratification and enable earlier intervention for patients at high risk of progression.

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.

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