Projekt Intraoperative Imaging and Machine Learning (IIML)10 ECTS
(englische Bezeichnung: Project Intraoperative Imaging and Machine Learning)

Lehrveranstaltungen:

• • Projekt Intraoperative Imaging and Machine Learning
    (Praktikum, 4 SWS, Katharina Breininger et al., Mi, 8:30 - 10:00, Hörsaal ZMPT; The on-premise part of the project will be held in ZMPT (Henkestraße). The first session (Oct. 20) will be via Zoom, further details will be shared at the beginning of the seminar.)

Empfohlene Voraussetzungen:

Students are required to have initial experience with deep learning and machine learning, e.g., from the module "Deep Learning".
This project 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 project, 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 connected topics, e.g., data privacy, code of ethics, prototype development, and UI design for surgeons. In the first part of the project, a predefined set of tasks will be implemented and validated (including a machine learning application and a UI for intraoperative use). In the second part of the project, the students will work on further extensions or adaptions of the prototype proposed by them.

At the end of the project, the students will have developed and documented a prototypical application for the intended 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

Studien-/Prüfungsleistungen:

Projekt Intraoperative Imaging and Machine Learning (Prüfungsnummer: 259157)

(englischer Titel: Project Intraoperative Imaging and Machine Learning)

Praktikumsleistung, benotet, 10 ECTS

weitere Erläuterungen:
Gewichtung der Modulnote: 2/3 abgegebene Aufgaben, 1/3 Zwischen- und Abschlussvortrag (Vortragsdauer 15 bzw. 30 Minuten)

Prüfungssprache: Deutsch oder Englisch

Erstablegung: WS 2022/2023, 1. Wdh.: SS 2023

1. Prüfer:

Katharina Breininger