This is a project which is currently making use of HPC facilities at Newcastle University. It is active.
For further information about this project, please contact:
This project aims to develop deep learning models to forecast the progression of Alzheimer's Disease (AD). By analyzing longitudinal T1-weighted MRI sequences, the research focuses on predicting anatomical changes in key brain regions (e.g., hippocampus) to identify early biomarkers for AD diagnosis and prognosis.
The workflow utilizes a Python-based deep learning environment managed by Miniconda. Key technologies include PyTorch for developing generative models (e.g., Rectified Flow, UNet) and 3D CNNs. The process involves GPU-intensive training and medical image preprocessing (registration, segmentation).