|
Einrichtungen >> Technische Fakultät (TF) >> Department Artificial Intelligence in Biomedical Engineering (AIBE) >>
|
W3-Professur für Image Data Exploration and Analysis
|
Medizintechnik II [MT2] -
- Dozentinnen/Dozenten:
- Florian Knoll, Bernhard Kainz
- Angaben:
- Vorlesung, 4 SWS, ECTS: 3,75, für Anfänger geeignet, für FAU Scientia Gaststudierende zugelassen, This course will be held online. The blackboard exercises will start in the first (!) week (29.4) and the computer exercises in the third week (9.5-13.5). Old lecture videos can be found at https://www.video.uni-erlangen.de/course/id/1022 and all further information can be found on StudOn https://www.studon.fau.de/crs4223415.html
- Studienrichtungen / Studienfächer:
- PF MT-BA 2
- Inhalt:
- VORL; 4 SWS; guest listeners; Prof. Dr. Florian Knoll , Prof. Dr. Bernhard Kainz
The lecture MT2 is aimed at students of medical technology and is one of the basic lectures in the field of informatics. Methods and devices that process and display the anatomy and function of the body for diagnosis and therapy are explained. Emphasis is placed on understanding and applying basic algorithms of medical imaging, such as segmentation, filtering, and image reconstruction. Modalities presented include X-ray systems, computed tomography (CT), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and ultrasound (US).
StudOn: https://www.studon.fau.de/crs4223415.html
- Empfohlene Literatur:
-
| | | Di | 10:15 - 11:45 | Zoom-Meeting | |
Knoll, F. Kainz, B. | |
|
Medizintechnik II Rechnerübung [MT2-RUE] -
- Dozentinnen/Dozenten:
- Johanna Müller, Florian Knoll, Marc Vornehm, Jinho Kim, Mischa Dombrowski, Bernhard Kainz
- Angaben:
- Übung, 2 SWS, für Anfänger geeignet, für FAU Scientia Gaststudierende zugelassen, This course will be held in person. The exercises will start in the third week (9.-.13.5.)
- Studienrichtungen / Studienfächer:
- PF MT-BA 2
- Inhalt:
- In selbstständiger, aber betreuter Projektarbeit werden die Inhalte der
Vorlesung direkt angewandt und dadurch vertieft. Dazu erarbeiten die
Studierenden eine technische Lösung für eine konkrete medizinische
Fragestellung.
Anmeldung zu den Rechnerübungen erfolgt ab dem 29.Apr 2022, 14:00 Uhr via Studon.
| | | Di | 12:00 - 14:00 | 01.155-113 CIP | |
Tutoren | |
| | Mi | 8:00 - 10:00 | 01.155-113 CIP | |
Tutoren | |
| | Mi | 10:00 - 12:00 | 01.155-113 CIP | |
Tutoren | |
| | Fr | 10:00 - 12:00 | 01.155-113 CIP | |
Tutoren | |
| | Fr | 14:00 - 16:00 | 02.151-113 a CIP, 02.151-113 b CIP | |
Tutoren | |
|
Project Representation Learning [PRL] -
- Dozentinnen/Dozenten:
- Bernhard Kainz, Johanna Müller, Mischa Dombrowski
- Angaben:
- Sonstige Lehrveranstaltung, 8 SWS, ECTS: 10, nur Fachstudium
- Termine:
- Zeit/Ort n.V.
- Studienrichtungen / Studienfächer:
- WPF AI-MA ab 1
WPF MT-BA-BV ab 1
WPF INF-BA ab 1
WPF DS-MA-DW ab 1
- Voraussetzungen / Organisatorisches:
- recommended:
Deep Learning ML Prof. Dr. Andreas Maier 2+2 5 x E
Pattern Recognition ML Prof. Dr. Andreas Maier 3+1+2 5 x E
Maschinelles Lernen für Zeitreihen ML Prof. Eskofier, Prof. Oliver Amft, Dr. Ch. Mutschler 2+2+2 7.5 x E
- Inhalt:
- Different projects in the area of (deep) representation learning are on offer. These reach from theoretical exploration of new data representation methods to practical evaluation of applications in, e.g., medical image analysis.
Example projects will be made available on the website of the IDEA Lab https://idea.tf.fau.eu/. Students may also propose their own projects, which will be coordinated and refined with the module lead during preliminary discussions.
- Empfohlene Literatur:
- A specific reading list will be established at the beginning of each project, general literature is listed below:
Quinn J, McEachen J, Fullan M, Gardner M, Drummy M. Dive into deep learning: Tools for engagement. Corwin Press; 2019 Jul 15. https://d2l.ai/
Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT press; 2016 Nov 18. https://www.deeplearningbook.org/
|
|
|
|
|
UnivIS ist ein Produkt der Config eG, Buckenhof |
|
|