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November 2021

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DBC SEMINARS


The DBC Seminars brings world leading computational scientists to present their work in a colloquium and to meet with faculty and students. The colloquium has broad attendance by faculty, staff, masters and PhD students from the University.
May 19th - DBC Seminar with 
Dr. Jonas Richiardi, Principal Investigator and Senior Lecturer at the Department of Radiology, Lausanne University Hospital (CHUV).

"Graph-based learning in medical imaging and imaging genetics"


When : May 19th - at 12h15


Where : Génopode - Auditoire A

Graphs are particularly well-suited for modelling relationships between parts of a system. This includes organs such as the brain, where graph representations are ubiquitous and offer an expressive language to model spatial, structural, and functional relationship from data obtained in medical imaging. Graphs enable substantial compression and smoothing of imaging data, which helps build predictive models from high-dimensional volumetric time series such as found in functional magnetic resonance imaging (brain) or CINE imaging (heart).

In this overview talk, I will first discuss estimation of graph edges, in the first instance more specifically the estimation of intrinsic correlations between brain regions. Drawing links with work in familial data and geostatistics, I will present some ongoing work about correlation estimators using replicates for 4D brain imaging data (3D + time), with a focus on robustness to local versus global noise. I will also show related ongoing work using image features to estimate edges in multiplex (multilayer) graphs for 4D cardiac imaging. This can represent cardiac structure and function, including morphology and motion.

Once a graph is obtained from the images of the organ, multiple approaches can be used to relate it to the clinical score or phenotype of interest. I will first briefly discuss network science approaches and show how they can be used to inform our understanding of organ function in health and disease, and then focus on machine learning approaches for graph data. I will discuss more specifically embedding approaches and graph neural networks, which have shown outstanding performance even in the limited data regime.

Finally, I will discuss how graphs computed from medical imaging data can be used in imaging genetics as an endophenotype representation and target for association studies, including genetic variation and transcriptomics. Increasingly, large databases suitable for imaging genetics studies are being collected (most notably UK Biobank), and I will briefly present our work in the area of heart failure subtyping using heart imaging and genetic data.

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