University of Cambridge > > AI in Medicine Seminar Series > AI in Medicine Seminar Series

AI in Medicine Seminar Series

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

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 also include a Q&A session to facilitate discussion and exchange of ideas among participants.

The next seminar will be held on the 27th of June 2023 at 11AM at the Seminar Room 12, School of Clinical Medicine, and will feature:

Distributional and relational inductive biases for graph representation learning in biomedicine – Paul Scherer, Department of Computer Science and Technology, University of Cambridge

The immense complexity in which biomolecular entities interact amongst themselves, with one another, and the environment to bring about life processes motivates the mass collection of biomolecular data and data-driven modelling to gain insights into physiological phenomena. Grand initiatives and continuing efforts have been coordinated to also structure our growing knowledge and understanding of biology (and beyond) within graph structured data. The (re)-emerging field of representation learning on graph structured data opens opportunities combine these streams of research to leverage prior knowledge on the structure of the data and construct models with improved performance or interpretability. This talk will discuss at a high-level how we may leverage the relational structures in biomedical knowledge and data to incorporate biologically relevant inductive biases into neural machine learning methods. This will be accompanied by considerations to make when designing relational inductive biases over some applications I have worked on that explore different scenarios under which graph structure arises in the data

Deep learning for segmentation of the Venous Tumour Thrombus in MRI – Robin Haljak, Department of Physics, University of Cambridge

An unusual hallmark of kidney cancer is the biological predisposition for vascular invasion, with the extension of the venous tumour thrombus (VTT) into the inferior vena cava occurring in 4-15% of cases. Automated segmentation of the VTT would be beneficial for the diagnostic evaluation of kidney cancer. However, the location, size and shape of the VTT are highly variable, making the automatic segmentation task difficult. Deep learning-based automatic segmentations of the VTT were created for the first time, using the nnU-Net segmentation framework. A two-stage localization-refinement-based 3D nnU-Net model is proposed to significantly increase the segmentation accuracy of the VTT in kidney cancer MRI scans. The proposed model involves two main steps. In the first step, the VTT is localised, and an initial segmentation is created. In the second step, the segmentation is expanded and refined to more accurately segment the VTT . Training and comparative experiments were conducted on the NAXIVA clinical trial data set.

Each session will involve two talks, followed by an interactive discussion with coffee and pastries! 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.

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

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

This talk is part of the AI in Medicine Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2023, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity