What will I learn in biomedical engineering

Topic: machine learning in medical technology

The Institute for Biomedical Technology is offering a seminar on the subject in the 2020/2021 winter semester "Machine Learning in Medical Technology" at. The aim of the seminar is for the students to work on a scientific topic independently and present the results in a presentation. The seminar thus offers the opportunity to learn and consolidate skills in the area of ​​developing topics and their presentation, which are of great use both for bachelor and master theses as well as in later professional life:

  • Independent development of a topic mainly from English literature
  • Conception and creation of a presentation
  • 25-minute presentation of the results in front of a group
  • Developing a culture of questions
  • Giving and taking feedback

The seminar aims to work through known and solved scientific problems in an understandable way. The content and style of presentation are discussed intensively in the group.

The topics will be presented on November 3rd, 2020. On 10.10. there is an introduction and the topics are assigned. On November 17 there is a lecture on presentation techniques and feedback methods. This is followed by 5-minute test presentations with a freely chosen topic. From December onwards, the selected topics will be presented by the students in 25-minute lectures. The lectures are recorded with a camera so that the students can critically deal with their own lecture. After each presentation there is an evaluated question and answer session and an unevaluated feedback session, the aim of which is to strengthen the lecturer's competencies.

The number of participants is limited to a maximum of 14. If there are more than 14 interested parties, students of electrical engineering and information technology (and specialization 3 - biomedical engineering) have priority. To register, simply come to the introductory event on November 3rd.

 

As part of the seminar topic "Machine Learning in Medical Technology" the following topics are expected to be awarded:

  • Feature extraction

  • Regression method

  • Classification procedure

  • Flat neural networks

  • Convolutional Neural Networks

  • Analysis of time signals: ECG diagnosis

  • Analysis of images: segmentation

  • Analysis of videos: application in the operating room

  • Distortion Variance Dilemma

  • Data augmentation

  • Generative Adversarial Networks

  • Explainability

  • Legal Aspects

  • Ethical aspects