Index

Deep Learning for Bias Field Correction in MRI Scans

Spoken Language Identification for Hearing Aids

Definition und Implementierung einer prototypischen Smart Home Schnittstelle für ein cloudbasiertes Energiemanagementsystem

Deep Learning-Based Breast Density Categorization in Asian Women

Improvements in SSL image-text learnings on CXR images

Deep Learning based Collimator Detection

Transformers vs. Convolutional Networks for 3D segmentation in industrial CT data

The current state of the art for segmentation in industrial CT are oftentimes CNNs.
Transformer based models are sparsely used.
Therefore, this project wants to compare the semantic segmentation performance of transformers (that include global context into segmentation), pure convolutional neural networks (that use local context) and combined methods (like this one: https://doi.org/10.1186/s12911-023-02129-z) on an industrial CT dataset of shoes like in this study: https://doi.org/10.58286/27736 .

Only available as Bachelors thesis / Research Project

Understanding Odor Descriptors through Advanced NLP Models and Semantic Scores

Generation of Clinical Text Reports from Chest X-Ray Images

Latent Diffusion Model for CT Synthesis

Latent diffusion model is a successful generative model in the modern computer vision researches. Modeling the generative process as image denoising, the diffusion models can generate realistic images in high quality and shows superior ability as the GAN-based models. In medical imaging, computed tomography (CT) is a well researched imaging modality and also widely applied in clinics. In this project, we will investigate the feasibility of modern diffusion models for the task of CT synthesis.