Attribut:Beschreibung-EN

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Biomedical image and signal processing  +
Basic concepts and methods of biomedical image and signal processing  +
Basic terms and methods of imaging  +
Examples of medical biosignals as well as filtering methods for biosignals and basic principles of processing biosignal and image data using (deep) neural networks (Deep Learning, CNN - Convolutronal Neural Networks).  +
Examples of medical images and their properties  +
Image processing methods (e.g. edge filter, median filter, FFT)  +
The registration and segmentation of medical images  +
Different visualization methods, their properties and characteristics (artefacts)  +
Application fields of image and signal processing  +
Biomedical modelling and simulation (add examples)  +
Basics of practical informatics/computer science for image and signal processing e.g. programming languages, software engineering  +
Basics of technical informatics/computer science for image and signal processing e.g. telecommunications, wireless data transmission technologies, virtual reality, multimedia  +
Important standards of medical informatics e.g. DICOM  +
Application scenarios for telemedical applications and their prerequisites  +
Basics of data mining and data analysis of primary and secondary data sources, principles of data mining, data warehouses (data sharing), knowledge management  +
Basics of data mining and data analysis of primary and secondary data sources, principles of data mining, data warehouses, knowledge management, (FAIR principles) - ideally on real conditions.  +
Queries on common databases  +
Measures to ensure high data quality (based on FAIR principles)  +
Requirements for information processing in clinical trials on EDC (Electronic Data Capture) e.g. from registries (German Cancer Registry)  +
Policies, ethical, regulatory and social aspects for the handling and exchange of big data  +