inf5406 - Medical Data Analysis with Deep Learning (Complete module description)

A basic theoretical understanding in machine learning, practical programming skills in Phyton, and basic knowledge in deep neural networks.

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

This module 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. These
topics are complemented by methodological focal points that
are particularly relevant for medical data analysis, such as interpretability, imbalanced or
sparsely labeled data. The students are supposed to work towards a final project of their
choice during the second half of the course.

For background reading, see Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and
medicine. Nature medicine, 28(1), 31-38. 

Relevant specialized readings will be recommended throughout the course.
 

at the end of the lecture period

50% daily project work, 50% either presentation or
written report