Index

Brain Tumor Segmentation with Focus on Complex Subregions

Exploring Narrative Representations in the Large Language Model BERT

Binary Mask Generation for Killer Whale Vocalizations

Machine Learning Based Optimization of Material Decomposition in Multi-Spectral Computed Tomography

Statistical Assessment of Deep Neural Networks in Industrial Applications

Mainframe Meets AI – Improving Legacy Code Generation Through Fine-tuning of Large Language Models

Noise and Call Characteristics of Killer Whales

Federated Learning for 3D camera-based weight & height estimation

Analysis of Recorded Single-Channel Patch-Clamp Timeseries Using Neural Networks Trained with Simulated Data

On how to learn and use the Detectability Index efficiently for CT trajectory optimisation

Optimizing the CT scan trajectory is crucial for industrial computed tomography as it can enhance the quality
of image reconstruction and reduce scanning time. However, determining the optimal trajectory is challenging
due to the large solution space of the nondeterministic polynomial time-hard optimization problem.
The objective of this study is to propose a suitable architecture for optimizing the trajectory of a robot-based
computed tomography (CT) system. This architecture aims to improve the quality of reconstructed images,
effectively representing the detectability index for a given task.
The goal of this optimization is to reduce artefacts in the CT images and potentially decrease the scanning time.
To achieve this objective, the proposed method requires a CAD model of the test specimen, simulates possible
X-ray projections and predicts the detectability index using a suitable regression neural network architecture.