Index

LLM-Centric Framework for Ontology-Driven SPARQL Query Generation in RAG for DICOM Databases

Enhancing small-sized video object detection through temporal information and synthetic data

proposal

Optimization of CT Image Volume in Dedicated Breast CT with Circle-Spiral Trajectory

This master’s thesis will focus on optimizing image reconstruction methods for dedicated breast CT scans using a circle-spiral trajectory. The aim is to improve post-reconstruction image quality and mitigate artifacts arising from the uneven distribution of information between the top and bottom regions, where the circular trajectory contributes more data than the helical section. The research will explore techniques to balance these differences and enhance overall image fidelity.

Diffusion Model-Based 3D CT Reconstruction for Arbitrary Trajectories

This master’s thesis will develop a novel approach using diffusion models for 3D CT reconstruction. The process will begin with denoising a highly blurred and noisy initial reconstruction from FBP or SIRT, aiming to enhance the quality of reconstructions obtained from a limited number of projection data on arbitrary trajectories. This research will focus on optimizing the denoising process to improve the output, advancing CT imaging capabilities with limited input data.

Distillation Knowledge of Large Language Models for Automotive HMI Applications

Speech-Based Classification of Parkinson’s Disease Under Acoustic Variability

Android App Respiratory Diseases

If you are interested, please send an email with your transcripts to paula.andrea.perez@fau.de with the subject: RepApp BA Thesis LME.

The students are expected to have proficiency in Android programming.

Gen AI: Speech Emotion Recognition

Apply Generative AI strategies in the field of Speech Emotion Recognition

Pre-requirements:

Pattern Analysis (Mandatory)
Deep Learning (Mandatory)
Advanced Depp Learning (Optional)
Speech and Language Understanding (Optional)
Seminar on pathological speech (Optional)

Please send your grades to paula.andrea.perez@fau.de

Universal Image Artifact Reduction via Heterogeneous Mixture of Experts

Abstract:
This master thesis proposes a novel unified framework for addressing various types of image artifacts through a heterogeneous Mixture of Experts (MoE) architecture. Unlike traditional approaches that tackle specific artifacts individually, our model leverages specialized expert networks, each designed to handle distinct degradation patterns, while maintaining the efficiency. The heterogeneous nature of the experts allows for optimal handling of diverse artifact types, from compression artifacts to motion blur, within a single unified model.