Open Projects in Machine Learning in Medicine

In a field that is so flooded with data as medicine, learning algorithms are providing countless exiting promises. For example, the data that we already have may contain all the information needed to understand and cure numerous diseases — we only need to learn how to make those information available. Help to uncover this entirely new world with us by choosing among one of our open master’s theses listed here.

Project	Supervisor
Transfer Learning for the collaborative segmentation platform Dafne	Francesco Santini
Segment By Example: a deep-learning approach to generic atlas-based segmentation	Francesco Santini
Deep-Learning Approach for Navigated Anterior Cruciate Ligament Surgeries	Philippe Cattin
Deep Learning for Automatization of Video-Analysis for Malaria Vector Behavioural Studies	Philippe Cattin
Incorporation of Uncertainty into Annotations for Deep Learning-based Image Segmentation	Philippe Cattin
Investigating the impact of fiber sensing density on the performance of eFBG shape sensors	Philippe Cattin
Investigating the impact of bending-induced phenomena on the performance of eFBG shape sensors	Philippe Cattin
Modulation of sleep spindles in healthy children compared to children with self-limited epilepsy with centrotemporal spikes	Philippe Cattin
Diffusion Models for Contrast Harmonization of Whole Head Magnetic Resonance Images	Julia Wolleb
Large Language Model Approach for Analyzing Medical Information on the Internet	Philippe Cattin
Automated segmentation of the Liver in 4DMRI	Philippe Cattin
Human-in-the-loop soft tissue palpation simulation based on graph neural-networks	Philippe Cattin
Improved Point Cloud Diffusion Models for Automatic Implant Generation	Philippe Cattin

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Open Projects in Machine Learning in Medicine

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