University of Basel

Department of
Biomedical Engineering

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.

ProjectSupervisor
Development of a predictive model for postoperative BMI and related diseasesPhilippe Cattin
Computer vision to automate bacterial single-cell analysisLucas Boeck (DBM)
Developing and Evaluating Vision-Language Models for Pediatric DermatologyAlexander Navarini
Shades of Equity: Using Generative AI to Reduce Skin Tone Bias in Dermatology DatasetsAlexander Navarini
Development of deep learning models for therapeutic drug monitoring of immunosuppressants in kidney transplant patients based on exhaled breath dataPablo Sinues
Predicting the timing of foot events during walkingMorgan Sangeux
Deep learning approach to predict concentration and clinical effects of antiseizure medication based on exhaled breath dataPablo Sinues
Transfer Learning for the collaborative segmentation platform DafneFrancesco Santini
Segment By Example: a deep-learning approach to generic atlas-based segmentationFrancesco Santini
Deep Learning for Automatization of Video-Analysis for Malaria Vector Behavioural StudiesPhilippe Cattin
Incorporation of Uncertainty into Annotations for Deep Learning-based Image SegmentationPhilippe Cattin
Investigating the impact of fiber sensing density on the performance of eFBG shape sensorsPhilippe Cattin
Investigating the impact of bending-induced phenomena on the performance of eFBG shape sensorsPhilippe Cattin
Modulation of sleep spindles in healthy children compared to children with self-limited epilepsy with centrotemporal spikesPhilippe Cattin
Automated segmentation of the Liver in 4DMRIPhilippe Cattin
Improved Point Cloud Diffusion Models for Automatic Implant GenerationPhilippe Cattin
AI-assisted ECG analysis for the Benefit of ED patientsPhilippe Cattin
Development of a user-friendly Application for a machine learning algorithm to predict the need for Hospitalization in Emergency Department patientsPhilippe Cattin
Development of a Deep Learning Model for Artifact Reduction in CBCT ImagesPhilippe Cattin
Diffusion models for Batch Style Transfer of Histology ImagesPhilippe Cattin
Domain-shift effects in real-world clinical dataPhilippe Cattin
Impact of different types of missing values on outcome prediction in Emergency MedicinePhilippe Cattin
Strict shape preserving VAE sampling of human vertebraePhilippe Cattin
AI Generation of Cerebral Perfusion MapsPhilippe Cattin
Finite Element Validation of retropatellar contact pressure in AI generated trochleasPhilippe Cattin

 

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