inf5406 - Medical Data Analysis with Deep Learning (Course overview)

Department of Health Services Research

6 KP

Module components

Semester courses Summer semester 2025

Examination

Lecture

2.01.5406 - Medical Data Analysis with Deep Learning
- Prof. Dr. Nils Strodthoff
- Juan Lopez Alcaraz
Monday: 14:00 - 16:00, weekly (from 07/04/25)
Thursday: 10:00 - 12:00, weekly (from 10/04/25)

This lecture provides insights into state-of-the-art deep learning method for the analysis of medical data. We cover a broad spectrum of data modalities and applications and try to discuss both methodological knowledge as well as the necessary medical background knowledge. In particular, we cover physiological time series (ECG, EEG), medical imaging (histopathology, CXR, CT/MRI), audio data (e.g. from digital stethoscopes), electronic health records, clinical text data as well as multimodal combinations of these data types. The students are supposed to work towards a final project of their choice during the second half of the course.

Exercises

2.01.5406 - Medical Data Analysis with Deep Learning
- Prof. Dr. Nils Strodthoff
- Juan Lopez Alcaraz
Monday: 14:00 - 16:00, weekly (from 07/04/25)
Thursday: 10:00 - 12:00, weekly (from 10/04/25)

This lecture provides insights into state-of-the-art deep learning method for the analysis of medical data. We cover a broad spectrum of data modalities and applications and try to discuss both methodological knowledge as well as the necessary medical background knowledge. In particular, we cover physiological time series (ECG, EEG), medical imaging (histopathology, CXR, CT/MRI), audio data (e.g. from digital stethoscopes), electronic health records, clinical text data as well as multimodal combinations of these data types. The students are supposed to work towards a final project of their choice during the second half of the course.

Hinweise zum Modul

Prerequisites	/
Prüfungszeiten	at the end of the lecture period
Module examination	50% daily project work, 50% either presentation or written report
Skills to be acquired in this module	Professional competence The students have an overview of the application areas of machine learning methods for analyzing medical data and can contextualize the issues within both the methodological and (bio)medical problem contexts. They are familiar with suitable algorithms and can apply them in practice. Methodological competence The students independently develop theoretical and practical concepts with the help of in-person events, provided materials, and specialized literature. Social competence The students can present solution approaches for problems in this area to the plenary and defend them in discussions. Self-competence The students are able to assess their own subject-specific and methodological competence. They take responsibility for their competence development and learning progress and reflect on these independently. In addition, they independently work on learning content and can critically reflect on the content. General competence goals + knowledge of data science/ML methods and its foundations ++ Ability to analyze problems, compare and select solution methods ++ formalizing problems mathematically, developing and implementing solutions, interpret their results + Data management and infrastructure skills + data presentation & discussion ++ interdisciplinary knowledge, thinking & communication + scientific communication skills (in particular with people outside the field of study) + independent research, project and time management