Index

Automated Patient Positioning (MRI) using nnUNet

CT Field-of-View Extension Dataset Simulation

Create a simulated dataset for CT FOV extension task using PYRONN

Unsupervised detection

Evaluate computer vision and detection methods.

Evaluation of stenosis detection in angiography images

Deep Learning-Based Classification of Skin Diseases: A Comparative Analysis of CNN and Transformer Architectures

Influence of Age in Neural Embeddings to Analyze Voice Disorders of Parkinson’s Disease Patients

Comparative Evaluation of Deep Learning Models for Chest X-ray Lesion Detection

Evaluate the detection performance on the public VinDR-CXR dataset.

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.