Cambridge AI in Medicine Seminar Series - April 2026

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 Tuesday 28 April 2026, 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:

AI-derived Whole-liver PDFF for Quantitative Assessment of Hepatic Steatosis - Lingjia Wang, PhD Candidate, Department of Radiology, University of Cambridge

Lingjia is a second-year PhD candidate in the Department of Radiology at the University of Cambridge, working on AI-based disease prediction and opportunistic screening in CT and MRI. Lingjia's research particularly focuses on translating automated imaging biomarkers into PACS-integrated clinical workflows.

Abstract: Hepatic steatosis exhibits spatially heterogeneous fat distribution that may be incompletely captured by routine region-of-interest (ROI)–based proton density fat fraction (PDFF) measurements. This retrospective study evaluated an AI-derived whole-liver PDFF framework in 556 abdominal liver MRI examinations (2023–2025). Automated whole-liver and Couinaud segment masks were generated using TotalSegmentator on 3D Dixon images and applied to PDFF maps, excluding voxels <0% or >55%. Agreement between whole-liver and radiologist-reported ROI PDFF was assessed using Bland–Altman analysis, and intrahepatic fat heterogeneity was analysed across Couinaud segments. Whole-liver PDFF showed good agreement with ROI PDFF (mean bias −0.35%; limits of agreement −5.48% to +4.78%). Segmental analysis demonstrated greater variability and lower mean PDFF in the left lobe compared with the right lobe. AI-derived whole-liver PDFF provides a standardised assessment of hepatic fat and captures spatial heterogeneity beyond single-ROI measurements.

TMoE: Task-Conditioned Mixture-of-Experts with Task-Query Heads for Medical Image Classification - Shuaiyu Yuan, PhD Candidate, Department of Radiology, University of Cambridge

Abstract: Medical image classification can cover heterogeneous datasets from various modalities, dimensionalities, anatomical focuses and di agnostic tasks. While training separate models is costly, performing a combined supervised learning with a fully shared backbone often suffers from negative transfer and limited feature extraction capacity. We pro pose TMoE, a Task-Conditioned Mixture-of-Experts with Task-Query Heads for multi-task medical image classification. TMoE augments a vi sion Transformer backbone by replacing feed-forward networks (FFN) in selected Transformer blocks with sparse MoE layers. A Top-k router, conditioned on a learned task embedding, dispatches tokens to subsets of experts, enabling task-dependent capacity allocation. We also intro duce task-query heads, where a per-task learned query performs cross attention over backbone tokens to produce a task-specific representation, followed by a lightweight task-specific classifier.

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 [email protected]

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

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Host or Publisher Cancer Research UK Cambridge Centre