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Vorlesungsverzeichnis >> Technische Fakultät (TF) >>
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Seminar Advanced Algorithms in Medical Image Processing (SemAAMIP)- Dozentinnen/Dozenten
- Prof. Dr.-Ing. habil. Andreas Maier, Florian Kordon, M. Sc., Prof. Dr.-Ing. Joachim Hornegger
- Angaben
- Seminar
2 SWS, ECTS-Studium, ECTS-Credits: 5
nur Fachstudium, Sprache Englisch
Zeit und Ort: Mo 8:15 - 9:45, KH 1.021
ab 21.10.2019
- Studienfächer / Studienrichtungen
- WPF INF-MA ab 1
WPF MT-MA-BDV ab 1
WPF CE-MA-TA-MT ab 1
- Voraussetzungen / Organisatorisches
- Registration via StudOn:
https://www.studon.fau.de/crs2686633.html
- Inhalt
- Deep Learning-based algorithms showed great performance in many fields of image procession and pattern recognition and compete with technologies such as compressive sensing and iterative optimization. The basis for the success of these algorithms is the availability of large amounts of data (big data) for training and of high computing power (typically GPUs). In this seminar we try to answer the question how typical problems in medical imaging might profit from deep learning concepts. In particular, we will aim at the data annotation problem. We have recently started a non-profit organization that is accepting medical data donations ( http://www.medicaldatadonors.org ). The donations also allow annotation via gamification over the Internet. Aim of this project seminar will be to develop a game for data annotation. In order to do so, we will look into the topics of deep learning, game design, programming in unity, and related topics.
- ECTS-Informationen:
- Credits: 5
- Zusätzliche Informationen
- Schlagwörter: algorithms; medical image processing
Erwartete Teilnehmerzahl: 10, Maximale Teilnehmerzahl: 10
www: https://www5.cs.fau.de/lectures/ws-1920/seminar-advanced-algorithms-in-medical-image-processing-semaamip/
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt über: StudOn
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2019/2020:
- Seminar Advanced Algorithms in Medical Image Processing (SemAAMIP)
- Institution: Lehrstuhl für Informatik 5 (Mustererkennung)
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