Projects
This page lists open student projects and also gives an overview of currently running and already finished projects. If you are interested in doing a master thesis or PhD with us do not hesitate to contact me.
Open Student Projects
Agentic AI for Multimodal Medical Data Analysis
Recent advances in multimodal foundation models enable the joint analysis of medical images, clinical text, laboratory values, signals, and other patient information. This thesis investigates an agentic AI framework that dynamically plans analysis workflows and coordinates specialized models and computational tools.
Running and finished Projects
Semi-Supervised Multi-Label Whole Heart Segmentation (finished, Elisabeth Rechberger)
Semi-supervised learning is a way of alleviating the requirement of lots of annotations, by also using information from images, where no annotations are available. The goal of this project is to apply semi-supervised learning techinques for semantic segmentation problems, specifically for multi-label whole heart segmentation.
Deep Active Learning for Semantic Segmentation (finished, Johannes Franz Spöcklberger)
Active Learning (AL) mitigates the problem of small annotated training dataset by focusing the annotation effort solely on the most informative samples in an iterative learning procedure. To accomplish this, an AL system proposes a subset of data samples in an unsupervised manner and requests annotations from a human expert. This subset is then added to the annotated data pool and used to train a model to solve the given task. When training finishes, the trained model is used to propose another subset of samples to be annotated and the procedure starts over until the predefined total annotation effort of the human expert is reached.
Detection of Infected Teeth in 3D CBCT Images (finished, Arnela Hadzic)
As a consequence of a bacterial infection, tooth associated infection is very common. Those pathologies are usually located in the surrounding of the root of the teeth. They can vary in diameter from a simple widening of the periodontal space up to several millimeters or more, being completely bone surrounded or perforating the adjacent anatomical borders. Furthermore, they potentially affect each of the around 30 roots per jaw. The manual location of those frequently requires a large amount of work, depending on the number of investigated teeth and the quality of the data set as well as on the education and experience of the doctor doing an examination. The aim of the project is to train deep convolutional neural networks (DCNN) to automatically recognize all the infected teeth in the 3D Cone Beam Computed Tomography (CBCT) image.
Deep Reinforcement Learning in Medical Image Applications (finished, Klemens Kasseroller)
By learning a sequence of actions that maximize the expected reward, deep reinforcement learning (DRL) brought significant performance improvements in many areas including games, robotics, natural language processing, and computer vision. It was DeepMind, a small and little-known company in 2013, that achieved a breakthrough in the world of reinforcement learning as they implemented a system that could learn to play many classic Atari games with human or even superhuman performance. Sill, it was until recently that DRL started to appear also in medical image applications for landmark detection, automatic view planning from 3D MR images, or active breast lesion detection.
Bayesian Neural Networks for Medical Image Applications: Age Estimation (finished, Stefan Eggenreich)
While standard deep convolution neural networks have recently shown unprecedented results that even go beyond human performance in computer vision tasks like classification, segmentation or detection, these methods are not capable of capturing model uncertainty. Being able to provide a prediction together with its uncertainty is of crucial importance for many medical applications that are related to decision making. Bayesian probability theory offers us mathematically grounded tools to reason about model uncertainty. Therefore, Bayesian deep learning, as a field at the intersection between deep learning and Bayesian probability theory, has recently attracted great interest of both computer vision and medical image communities.