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: https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09
Meeting ID: 990 5046 7573 and Passcode: 617729
