2018-2019 / STAT0722-1

Introduction to medical statistics

Duration

10h Th, 5h Pr

Number of credits

 Master in biomedical engineering (120 ECTS)2 crédits 
 Master in physics (120 ECTS)2 crédits 

Lecturer

Christophe Phillips

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Methods to process and analyse medical images, mainly oriented towards statistical analysis in functional and anatomical neuro-imaging

Learning outcomes of the learning unit

The aim is to have an overall understanding of the processing steps of medical images, with a view to functional and strucutral neuro-imaging: - image spatial preprocessing: realignment, normalisation, coregistration, segmentation - statical modeling with the "General Linear Model" and estimation of its parameters - statistical inference about these parameters and correction for the multiple comparison problem - other methods: non-parametric statistic, connectivity, dynamic causal modeling, Bayesian inference, etc.

Prerequisite knowledge and skills

Notions of image and signal processing, and/or experience in medical imaging, and/or statistical analysis

Planned learning activities and teaching methods

Mode of delivery (face-to-face ; distance-learning)

The course is taught ex cathedra at the Cyclotron Research Centre.

Recommended or required readings

You can find most of the material seen at the course on the SPM website.

Assessment methods and criteria

Functional neuroimaging project realised in collaboration with the CRC under supervision of a PI. Or oral presentation and discussion of a recent scientific article related to the content of the cours.

Work placement(s)

Organizational remarks

The course is taught in English.

Contacts

Christophe Phillips, c.phillips@uliege.be

Items online

Slides, course 2018
Slides presented during the course.