Index

Sinogram Analysis Using Attention U-Net: A Methodological Approach to Defect Detection and Localization in Parallel Beam Computed Tomography

The emergence of deep learning has ushered in a transformative era within the realm of image processing, notably in the context of Computed Tomography (CT). Nevertheless, it is noteworthy that a majority of image processing algorithms traditionally rely on processed or reconstructed images, often overlooking the raw sensor data. This thesis, however, shifts its focus toward the utilization of unprocessed computed tomography data, which we refer to as sinogram. Within this framework, we present a comprehensive three-step deep learning algorithm, leveraging a UNet-based architecture, designed to identify and analyze defects within objects without resorting to image reconstruction. The initial phase entails sinogram segmentation, facilitating the extraction of defect masks within the sinogram. Subsequently, instance segmentation is employed to effectively segregate these masks, resulting in their individualization. Lastly, the isolated masks are subjected to thorough defect analysis. Our research endeavors encompass comprehensive experimentation, conducted on both simulated datasets and real-world data.

Automatic recognition of bavarian dialects

Let Transformer Decoder for Text Spotting on Historical Maps

Brain Tumour Segmentation Focused on Complex Sub-regions

Deep Reinforcement Learning Based Emergency Department Optimization

Fine-tune large language models for radiation oncology

Statistical Assessment of Deep Neural Networks in Industrial Applications

Optimization and Evaluation of Deformable Image Registration Accuracy for Computed Tomography in Radiation Therapy

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

Dilemma Zone Prediction with Floating Car Data by Using Machine Learning Approaches