Cambridge MedAI Seminar Series - September 2025

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 30 September 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:

Automated Lesion Segmentation of Stroke MRI Using nnU-Net: A Comprehensive External Validation - Dr Tammar Truzman, Postdoctoral Fellow, MRC Cognition and Brain Sciences Unit, University of Cambridge

Dr Tammar Truzman is a Postdoctoral Fellow at the MRC Cognition and Brain Sciences Unit, University of Cambridge, working with Prof. Matt Lambon Ralph and Dr. Ajay Halai. Her research focuses on language assessment and recovery in people with aphasia, combining neuroimaging, language rehabilitation, and computational modeling. She is also a licensed speech-language pathologist with expertise in language therapy and clinical translation.

Abstract: Accurate lesion segmentation is a critical step in stroke neuroimaging, both for advancing theoretical understanding of brain–behavior relationships and for enabling clinical applications. Deep learning methods have recently shown promise, but external validation across diverse datasets remains limited. In this talk, I will present a comprehensive evaluation of nnU-Net for stroke lesion segmentation across multiple acute and chronic datasets. I will discuss factors influencing model performance and generalization, including imaging modality, dataset size and quality and lesion volume. The results highlight both the potential and the current limitations of automated segmentation tools for translational use in stroke and aphasia research.

Deep Learning-Based Follicle Growth Prediction using a Transformer Architecture - Artsiom Hramyka, Postdoctoral Fellow, University of Cambridge

Artsiom is a Postdoctoral Researcher in Computer Science and Medicine at the University of Cambridge, where his work involves applying artificial intelligence and simulation modelling to solve complex healthcare problems. This research builds upon his doctoral work at the University of St Andrews, where he is completing his PhD thesis on the application of novel analytical frameworks and AI in healthcare. Currently, his primary focus is on the early detection of cancer as part of the CRUK International Alliance for Cancer Early Detection (ACED). In this role, he develops and calibrates multistate models that simulate the natural history of malignant cancers to evaluate and optimise screening strategies. His research also extends to other areas of medicine, including active collaborations where he applies machine learning to enhance fertility treatments with Imperial College London and to analyse treatment data in paediatric oncology with the Charlotte Maxeke Johannesburg Academic Hospital.

Abstract: Traditional methods for predicting ovarian follicle growth rely on the clinically unfeasible assumption of tracking individual follicles between ultrasound scans. This research introduces a novel approach that overcomes this limitation by predicting the entire follicle size distribution. We developed a decoder-only, GPT-like Transformer architecture to autoregressively forecast future follicle profiles from sequential scan data. Model performance was evaluated using distribution-level metrics, including Earth Mover's Distance (EMD) and Chi-Square distance, across three clinically relevant scenarios simulating different data availability. Systematic hyperparameter optimisation resulted in a performance increase, a 10.2% improvement in EMD for short-term predictions. A key finding is the robust performance of the model when using only a single initial scan, demonstrating its potential utility in cases with missed appointments and highlighting the importance of training-inference consistency. This work represents the first application of a Transformer architecture for distribution-level follicle prediction, offering a more realistic tool for clinical decision support in assisted reproductive technology.

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/