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| Semester | Frühjahrsemester 2022 |
| Angebotsmuster | Jedes Frühjahrsem. |
| Dozierende | Marcel Lüthi (marcel.luethi@unibas.ch, BeurteilerIn) |
| Inhalt | Statistical shape models are models, which represent the typical variability of an organ or shape in terms of a probability distribution. The most important application of shape models is in medicine, where they are used for the analysis of medical images, surgery planning or the design of implants. There are, however, also many application in other fields, such as bio-mechanics, anthropology or forensics. In this course the participants will address a classical problem in the forensics sciences using shape modelling: Given a fragment of a bone, what can we say about the person? To solve this mystery, we will combine methods from mathematics, statistics and machine learning. More precisely, the participants will learn about Gaussian processes, Bayesian modelling and Markov Chain Monte Carlo methods. All the mathematical methods will be practically explored and visualized using the open source software library Scalismo. |
| Lernziele | At the end of the course the students should be able to - describe how shapes can be analyzed using statistics . - apply the mathematical concept of a Gaussian process to model anatomical shapes - follow a principled Bayesian workflow for analysing shapes. - develop programs for medical image analysis using the open source software scalismo |
| Literatur | Links to related literature will be given as part of the course. |
| Bemerkungen | Requires participation in the online course "Shape Modelling - Computing the human anatomy" (www.futurelearn.com/courses/statistical-shape-modelling) |
| Weblink | Course Webpage |
| Teilnahmebedingungen | Open to Master and PhD students with basic knowledge in probability theory and statistics, linear algebra as well as programming experience in a modern programming language (e.g. Java or C++) |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | Online-Angebot obligatorisch |
| HörerInnen willkommen |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| wöchentlich | Dienstag | 14.15-16.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Datum | Zeit | Raum |
|---|---|---|
| Dienstag 22.02.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 01.03.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 08.03.2022 | 14.15-16.00 Uhr | Fasnachtsferien |
| Dienstag 15.03.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 22.03.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 29.03.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 05.04.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 12.04.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 19.04.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 26.04.2022 | 14.15-16.00 Uhr | Spiegelgasse 1, Seminarraum 00.003 |
| Dienstag 03.05.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 10.05.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 17.05.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 24.05.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 31.05.2022 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Module |
Doktorat Informatik: Empfehlungen (Promotionsfach: Informatik) Modul: Applications of Distributed Systems (Masterstudium: Computer Science) Modul: Concepts of Machine Intelligence (Master Studienfach: Computer Science) Modul: Methods of Machine Intelligence (Masterstudium: Computer Science) |
| Leistungsüberprüfung | Lehrveranst.-begleitend |
| Hinweise zur Leistungsüberprüfung | The final grade will be computed based on the result of a practical project and a written exam. The project contributes 50% and the written exam 50% to the final grade. Written exam, expected date: 7 July 2022, 2-4, Spiegelgasse 5, room 05.002. |
| An-/Abmeldung zur Leistungsüberprüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,5 |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch |
| Anbietende Organisationseinheit | Fachbereich Informatik |