Master's Thesis
Your Thesis
In order to begin the development of your master's thesis, you must have already accumulated 80 ECTS credits from your courses.
During the last six months of your studies, you must dedicate your time and effort to working on your thesis. This period, encompassing 30 ECTS credits, is crucial for conducting research and crafting your thesis document.
As an enrolled student in the master's program "Artificial Intelligence in Medicine", you can access all the resources and guidance in the "AIM Master's Thesis" course on the university’s E-learning platform ILIAS. Here, you will find current academic research project proposals, past thesis projects for reference, and essential information about the application process. Students also have the opportunity to organise a master thesis project within the frame of an internship in a company. This must then be approved by the study coordination and program management.
Before graduation, all students must present their thesis projects.
My Thesis in 180 Seconds - Videos
Master's Thesis Presentations
You are cordially invited to attend the following presentations:
Sadly, there are no upcoming presentations
List of Former Theses Projects
- February 11, 26 at 15h15: Nirosh Sivanesan: "Benchmarking Foundation Models for Medical Imaging with Contextual Integration of DICOM Metadata" (PDF, 244KB)
- February 11th, 26 at 13h00: Alicja Krzeminska-Sciga: "Fundus Image Segmentation for Clinical Application – Quantitative Analysis of Retinal Structures and Pathologies" (PDF, 995KB)
- Januar 21nd, 26 at 11h00: Tea Pula: "Non-Invasive Detection of Rapid and Reversible Intracranial Pressure Changes From Retinal Fundus Images" (PDF, 2.3 MB)
- January 12th, 26 at 10h30: David Baier: "Learning tissue differentiation based on polarimetric imaging" (PDF, 209KB)
- December 23nd, 25 at 11h00: Rebecka Fahrni: "Post-Training Vision-Language Models with Reinforcement Learning and Verifiable Rewards for Abnormality Grounding and Detection in Radiology" (PDF, 245KB)
- December 17th, 25 at 15h00: Rachel Robles: "Causal Modeling Analysis of Infection Impact on Health Trajectory Using Symptom and Biometric Data" (PDF, 710KB)
- November 19th, 25 at 10h00: Darius Kaufmann: "Adaptive Control Software for Drones: A Self-Learning Approach" (PDF, 1.8 MB)
- November 28th, 25 at 15h30: Isabella Torres Revelo: "Automatic Acetabular Fracture Classification from CT using Deep Learning Techniques" (PDF, 278KB)
- October 22nd, 25 at 14h00: Tudorita Zaharia: "Subtype and Stage Inference in Amyotrophic Lateral Sclerosis using Unsupervised Learning" (PDF, 307KB)
- July 8th, 25 at 13h30: Heather DiFazio: "Mueller matrix polarimetry for automated assessment of dewaxed pancreatic tissue using deep learning" (PDF, 123KB)
- December 10, 24 at 1pm: Hasti Hamedi presents "AI-based Analysis of Abdominal Ultrasound Images to Support Medical Diagnosis in Emergency Departments" (PDF, 196KB)
- November 18, 24 at 2pm: Shunyu Wu: "Applying active learning methods on state-of-the art pretrained models" (PDF, 198KB)
- September 12, 24 at 10 am: Vinzenz Uhr: "Diffusion-Based Filling and Synthesis of Multiple Sclerosis Lesions" (PDF, 172KB)
- September 2, 24 at 4 pm: Jiahui Yu: "Machine Learning Made Easy (MLme) 2.0: Enhancing Analytical Capability with Regression Analysis" (PDF, 489KB)
- August 13, 24 at 13:00: Marta Colmenar Herrera: "Advancing Glaucoma Progression Prediction with Machine Learning Models" (PDF, 450KB)
- January 25, 24 at 11:00 am: Tim Graf "Machine Learning Analysis of Automated Fluorescence Flow Cytometry Data for Fast and Efficient Microbiological Analysis of Urine Samples" (PDF, 528KB)
- September 22, 23 at 11:00 am: Raphael Joost "Deep Learning based Monte Carlo Dose Denoising for Radiation Therapy" (A137, Inselspital Bern, Radio Oncology) (PDF, 174KB)
- September 5, 23 at 11:00 am: Yanis Schärer: "Proliferative Diabetic Retinopathy Detection with Multimodal Deep Learning" (F502, ARTORG Center, Murtenstrasse 50) (PDF, 4.4 MB)
- August 31, 23 at 10:00 am: Chris Rüttimann: "Development of a User Interface for Deep-zoom Whole Slide Images to Overlay Predictions from Deep Learning Models" (H431, Institute for Tissue Medicine and Pathology, Murtenstrasse 31) (PDF, 102KB)