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. 

This lecture provides an insight into current methods of deep learning  for analyzing medical data. A wide spectrum of data modalities and  application areas is covered, ranging from medical imaging  (X-ray/histopathology/CT/MRI) to medical time series (EKG/EEG/audio), and extending to electronic health records, medical text data, and finally, the multimodal integration of various data sources. These topics are complemented by methodological focal points that are  particularly relevant for medical data analysis, such as interpretability, imbalanced or sparsely labeled data

at the end of the lecture period

written / oral exam / project work