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Strukturbaum
Keine Einordnung ins Vorlesungsverzeichnis vorhanden.
Veranstaltung ist aus dem Semester
WiSe 2022/23
, Aktuelles Semester: SoSe 2024
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Machine Learning in Medicine - Theory & Practice
Sprache: Deutsch
Keine Belegung möglich
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(Keine Nummer)
Seminar/Praktikum
WiSe 2022/23
4 SWS
keine Übernahme
ECTS-Punkte: 9
https://www.uni-due.de/mathematik/agkraus/johannes_kraus_teaching.php
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Lehreinheit:
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Mathematik
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Teilnehmer/-in
erwartet : 10
Maximal : 20
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TM M.Sc., Technomathematik (Master of Science)
(
1.
Semester )
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WM M.Sc., Wirtschaftsmathematik (Master of Science)
(
1.
Semester )
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M M.Sc., Mathematik (Master of Science)
(
1.
Semester )
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AI MA, Angewandte Informatik (Master)
(
1.
Semester )
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W10, Wirtschaftsinformatik (Master of Science)
(
1.
Semester )
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S2, Angewandte Informatik - Systems Engineering (Master of Science)
(
1.
Semester )
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B-MedT-19, Medizintechnik
(
1.
Semester )
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Master of Science Technomathematik, Abschluss 87, Master of Science Technomathematik (87791)
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Master of Science Wirtschaftsinformatik, Abschluss 87, Master of Science Wirtschaftsinformatik (87721)
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Master of Science Mathematik, Abschluss 87, Master of Science Mathematik (87105)
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Master of Science Angewandte Informatik (Ingenieur- oder Medieninfor, Abschluss 87, Master of Science Angewandte Informatik (Ingenieur- oder Medieninfor (87AIM)
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Master of Science Angewandte Informatik - Systems Engineering, Abschluss 87, Master of Science Angewandte Informatik - Systems Engineering (87165)
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Master of Science Medizintechnik, Abschluss 87, Master of Science Medizintechnik (87MET)
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Zugeordnete Lehrpersonen:
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Kraus
,
Kleesiek
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Termin:
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Mittwoch
14:00
-
18:00
wöch.
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Raum :
WSC-N-U-4.03
Weststadtcarree
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| Bemerkung: |
<div class=""><div class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div class=""><div class=""><div class=""><div class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div class=""><div class=""><div class=""><div class=""><span style="font-size: 14pt;">In the seminar "Machine Learning in Medicine - Theory & Practice" state-of-the-art supervised segmentation methods for medical imaging, e.g., computed tomography (CT), will be explored. After an introduction to the data we will create a common evaluation pipeline. Then, in small teams, we will study selected key publications of the most popular network architectures, such as U-Net, Densenet, and ResNet. The students will implement and evaluate the algorithms on the medical CT data. The results of the teams will be compared and the influence of hyperparameter choices will be investigated. Note, there will be a strong focus on the practical implementation of the algorithms, requiring a sound programming knowledge in python.</span></div></div></div></div></div></div></div></div></div></div> |
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