Swiss Institute for Translational and Entrepreneurial Medicine (sitem-insel)

Artificial Intelligence in Medical Imaging

Module 2 - Statistics and Programming

 

Content

Module 2 is providing preliminary knowledge on statistics and programming necessary to tackle the following modules. Participants will acquire basics in Python programming that are relevant for data analysis.

Learning objectives

Upon successful completion of this module, participants know the relevant concepts from applied linear algebra and from probability and statistics. They also have a basic understanding of Python programming for the purpose of data analysis.

Learning content

This module includes basics in applied linear algebra, introduction to probability and statistics, as well as Python programming I to III, and linear regression in Python. 

Credits

2 ECTS

Fee

The cost of this standalone module is 1'600.– SFr.

Reference

02.002

 

Module Leaders

Senior Researcher, Support Center for Advanced Neuroimaging,University Hospital Inselspital, Bern

Dr. Richard McKinley

Richard McKinley is a Senior Researcher at the Support Centre for Advanced Neuroimaging, a research group in the University Clinic for Diagnostic and Interventional Neuroradiology in the Inselspital. Since 2014 he has been a researcher at the Inselspital, where he develops machine-learning techniques for interpreting, labeling and quantifying neuroimaging.

Postdoctoral Researcher, Support Center for Advanced Neuroimaging, University Hospital Inselspital Bern

Dr. Raphael Meier

Raphael Meier has a background in biomedical engineering and informatics. He is a postdoctoral researcher at the Support Center for Advanced Neuroimaging of the University Institute for Diagnostic & Interventional Neuroradiology at the Inselspital in Bern. Raphael’s research is focused on the application of machine learning to solve problems in brain image analysis. In particular, he is interested in techniques important for translation of machine learning methods into clinics such as model interpretability, uncertainty quantification and model validation. Furthermore, he is interested in imaging biomarker discovery for brain tumors and stroke.