Index
Spectral Plaque Analysis from Photon Counting CT
Frequency Domain Hierarchical Vision Transformer-based Perceptual Loss
This project focuses on improving image processing tasks, such as super-resolution or image restoration, by employing a novel feature comparison method. It leverages a Hierarchical Vision Transformer to extract multi-scale feature representations from images. These features capture both local and global information at various levels of abstraction. Crucially, these extracted features are then transformed into the frequency domain, likely via a Fast Fourier Transform (FFT) or similar method. The comparison between the generated image and the target image occurs in this frequency space. By analyzing differences in magnitude and/or phase across different frequency bands, the model can better understand and rectify discrepancies in texture, detail, and overall structure. This approach aims to produce perceptually superior results by guiding the model to reconstruct images that are more aligned with the frequency characteristics of the target, leading to improved visual quality, especially in terms of sharpness and fine-grained details.
Emotion recognition Project
Benchmarking Automatic Speaker Anonymization Methods for Healthy Speech
Evaluate Simpleshot: Simple implementation of few-shot classification on CXRs
Evaluate simpleshot – a simple implementation of few -shot classification on various findings on CXRs.
This report demonstrates that given sufficient pretraining data, we can achieve comparable classification results for complex findings such as pneumothorax , etc with less than 10 images.
Text Embedddings in Pathological Speech
Evaluation of Quantum Annealing based Projection Selection for Emission Tomography
Differential privacy for securing speech-based deep learning models against gradient inversion attacks
A Hybrid TransUNet-TransFuse Architectural Framework for Ice Boundaries Extraction in Radio-Echo Sounding Data
Evaluation of the TransSounder [1] architecture for direct ice boundaries extraction from radio-echo sounding data.
References
[1] Ghosh, R., & Bovolo, F. (2022). Transsounder: A hybrid transunet-transfuse architectural framework for semantic segmentation of radar sounder data. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-13.
3D CT Image Visualization using Blender
Introduction:
This project aims to develop a streamlined pipeline for 3D CT images visualization using Blender and Bioxel Nodes. You’ll create a step-by-step process to import, process, and render medical imaging data, resulting in high-quality scientific visualizations. This 5 ECTS project will enhance your technical skills and ability to visualize complex medical data.
Source: https://omoolab.github.io/BioxelNodes/0.1.x/
Prospective candidates are warmly invited to send their CV and transcript to yipeng.sun@fau.de.