[Statlist] Reminder: ETH/UZH ZüKoSt: Seminar on Applied Statistics by Björn Menze, Universität Zürich, 06.05.2022

[Maurer Letizia]

We are glad to announce the following talk in the ETH/UZH ZüKoSt: Seminar on Applied Statistics:

"On tumors and vessels in medical image data"   
by Björn Menze, Universität Zürich

Time: Friday,  06.05.2022 at 15.15 h
Place: ETH Zürich, HG G 19.1

Abstract: Biomedical image data offers quantitative information about health, disease, and disease progression under treatment - both at the patient and at the population level. Computational routines are instrumental in extracting this information in a structured fashion, typically following a succession of image segmentation, 'radiomic' feature extraction, and predictive modeling with respect to a given image marker or disease-​related outcome. This pipeline can also be complemented by a functional and patient-​specific modeling of the features or processes underlying the given image observations, for example, the tumor-​growth underlying a set of magnetic resonance scans acquired prior to and after treatment. I will talk about this biomedical image data processing pipeline, focusing on two aspects of our work in Zurich: the analysis of tumor images using patient-​adapted tumor growth models, and the extraction of whole brain vascular networks from 3D image data. I will demonstrate how to extract PDE model parameters from image observables using CNNs and show how we extract sparse physical networks from noisy image volumes using different learning strategies. I will also comment on data that made publicly available for both applications.

Seminar website: https://math.ethz.ch/sfs/news-and-events/seminar-applied-statistics.html


Organisers: F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Kalisch, M. H. Maathuis, M. Mächler, L. Meier, M. Robinson, C. Strobl, S. van de Geer