Cambridge MedAI Seminar Series

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

• Dr. Jonathan Weir-McCall and Konstantin Hemker
• Tuesday 21 May 2024, 12:00-13:00
• Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge.

If you have a question about this talk, please contact Ines Machado.

The Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke’s are pleased to announce a seminar series on Artificial Intelligence (AI) in Medicine, which aims to provide a comprehensive overview of the latest developments in this rapidly evolving field. As AI continues to revolutionize healthcare, we believe it is essential to explore its potential and discuss the challenges and opportunities it presents.

The seminar series will feature prominent experts in the field who will share their research and insights on a range of topics, including AI applications in disease diagnosis, drug discovery, and patient care. Each seminar will involve two talks, followed by an interactive discussion with a light lunch from Aromi! We hope that this seminar series will be a valuable platform for researchers, practitioners and students to learn about the latest trends and explore collaborations in the exciting field of AI in Medicine.

The next seminar will be held on 21 May 2024, 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 11:50. This month will feature the following two talks:

Big Data and AI in Cardiac Imaging – Making a difference? – Jonathan Weir-McCall, Assistant Professor, Department of Radiology, University of Cambridge

Jonathan is a University Lecturer at the University of Cambridge and an Honorary Consultant Cardiothoracic Radiologist at the Royal Papworth Hospital. His research interests lie in the use of cardiovascular CT and MRI for better understanding how these can be used to improve patient treatment and outcomes in structural and coronary artery disease. He has authored >130 peer reviewed publications, co-authored the SCCT guidelines on the role of CT in the assessment for transcatheter aortic valve insertion, and the BSCI /BSTI guidelines on the reporting of calcification on routine chest CT. He sits on the executive committee of the BSCI , guideline committee of the SCCT , and Diagnostic Advisory Committee of NICE .

Abstract: AI and advanced analytics are reaching clinical practice with significant opportunities, but also challenges in determining their real world impact and efficacy. In cardiac imaging, advanced analytics using AI and computational fluid dynamics are being routinely used in clinical care. While small scale randomised control trials present promising insights into their potential benefits, real world data is lacking. Leveraging national datasets we analyse the impact of these technologies in the UK, examining the impact of one AI-augmented CT tool on healthcare behaviours and patient outcomes.

Learning structures in multimodal pathology – Konstantin Hemker, PhD Candidate, Computer Laboratory, University of Cambridge

Konstantin is a PhD student in the Computer Lab at the University of Cambridge focussing on multimodal representation learning for biomedical data modalities. In particular, he is looking at how fusion models can provide multi-scale context in computational pathology. Before starting his PhD, Konstantin worked as a Senior Data Scientist in the Healthcare and Pharmaceuticals practice at the Boston Consulting Group, focussing on drug yield optimisation of active ingredients in antibody treatments and radiocontrast agents. He holds Master’s degrees in Computer Science from Imperial and Cambridge and an undergraduate degree from the London School of Economics.

Abstract: Integrative modelling of multiple data structures (such as images, graphs, sequences, or tabular data) in the same model is a common challenge for machine learning approaches in biomedical domains. This challenge arises from a lack of shared semantics between modalities, one-to-many relationships, missing modalities, and data sparsity. Meanwhile, multi-scale context can provide important information about the tumour microenvironment in fields such as computational pathology and consequently help train better predictive models. This talk will cover state-of-the-art multimodal representation learning methods that can learn from multiple data distributions, capture cross-modal relationships, and handle missing modalities whilst maintaining structural information from each modality for predictive tasks in pathology.

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

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.