Seminar Intraoperative Imaging and Machine Learning (IIML)5 ECTS
(englische Bezeichnung: Seminar Intraoperative Imaging and Machine Learning)
(Prüfungsordnungsmodul: Seminar Intraoperative Imaging and Machine Learning)

Lehrveranstaltungen:

• • Seminar Intraoperative Imaging and Machine Learning
    (Seminar, 2 SWS, Katharina Breininger et al., Mi, 8:30 - 10:00, 09.150)

Empfohlene Voraussetzungen:

Students are required to have initial experience with deep learning and machine learning, e.g., from the module "Deep Learning".
This seminar is recommended for Master's students.

Inhalt:

For many applications, techniques like deep learning allow for considerably faster algorithm development and allow to automate tasks that were performed manually in the past. In medical imaging, a large variety of time-consuming tasks that interfere with clinical workflows has the potential for automation. However, at the same time new challenges arise like data privacy regulations and ethics concerns.
In this seminar, we want to develop an application that allows for the automation of an X-ray based intraoperative planning or measurement procedure from a holistic perspective. To this end, we will invite a surgeon to explain the medical background and visit the operating room to understand the surgeons’ needs while performing the task. Having understood the underlying medical problem, we will look into topics of data privacy, code of ethics, prototype development, and UI design for surgeons. Furthermore, we will touch regulatory requirements necessary for releasing software to clinics.
At the end of the seminar, the students will have developed and documented a prototypical application for the indented intraoperative use case.

Lernziele und Kompetenzen:

Students will be able to

• visit an operation room, following the rules of such an environment
  

• perform their own literature research on a given subject
  

• independently research this subject according to data privacy and ethical standard
  

• present and introduce the subject to their student peers
  

• give a scientific talk in English according to international conference standards
  

• describe their results in a scientific report

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science)
   (Po-Vers. 2013 | TechFak | Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science) | Gesamtkonto | Seminar, Masterarbeit | Seminar im Masterstudium | Seminar Intraoperative Imaging and Machine Learning)

Studien-/Prüfungsleistungen:

Seminar Intraoperative Imaging and Machine Learning (Prüfungsnummer: 76071)

(englischer Titel: Seminar Intraoperative Imaging and Machine Learning)

Prüfungsleistung, Seminarleistung, benotet, 5 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

weitere Erläuterungen:
Die Gesamtnote setzt sich zu 50% aus der Bewertung des Vortrags und zu 50% aus der Bewertung der Ausarbeitung / Implementierung zusammen. Ziel des Seminars ist die verständliche Aufbereitung eines Themas für andere Studierende. Die Vortragsdauer beträgt 30 Minuten. Ziel ist es, diese möglichst genau einzuhalten. Die Ausarbeitung umfasst 4-8 Seiten im Stil von IEEE-Konferenzbeiträgen.
Vortrag und Ausarbeitung sollten auf Englisch erfolgen. Falls die Anzahl der Teilnehmenden dies erlaubt, arbeiten die Studierenden in Gruppen von 3-4 Personen.
The overall grade consists of two parts: The evaluation of a 30 minute talk (50%) and the evaluation of a report paper / implementation (50%). The goal of the seminar is to prepare a topic for other students in an accessible way. The goal is to keep this time as closely as possible. The report paper comprises 4-8 pages in the style of IEEE conference contributions.
Talk and seminar paper should be in English. If the number of participants allows, students will work in groups of 3-4.

Prüfungssprache: Deutsch oder Englisch

Erstablegung: SS 2021, 1. Wdh.: WS 2021/2022

1. Prüfer:

Andreas Maier