Extracting Knowledge from Medical Data with Machine-Learning
We develop novel machine learning and artificial intelligence approaches to retrieve knowledge of medical data including images, text and voice.
Development and evaluation of workflows and algorithms to extract multi-modal information from medical data. Medical data can encompass time series from intensive care units, images from optical coherence tomography or ultra-sound or simple text documents.
Note: Raw and labeled data are available in sufficient quantity
- Bootstrapping & familiarize with state-of-the-art approaches
- Develop promising approaches, e.g.
- generative models: GANs, VAE
- object classification, segmentation and detection in images
- causality models: PGMs
- anomaly detection
- Sentiment analysis
- Network pruning
- Establish simple workflow including data collection, pre-processing, analysis, benchmarking and optimizing
- Evaluate developed models w.r.t. benchmarking metrics
- 30% theory, 70% software engineering
- Master’s thesis, 1-2 students
- Prior knowledge recommended in Python, AI and ML required
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