Parkinson's Disease Progression
This project addresses limitations in Parkinson’s Disease (PD) research by creating synthetic data that aims to simulate changes in facial features associated with PD progression. Using diffusion models and inpainting techniques, we developed a pipeline to generate realistic image pairs representing the transition from healthy to PD-affected facial states. Additionally, we utilized evaluations based on training classification models on the synthetic data. We also explored Model generalization using subset of FFHQ dataset and saw improvement by 5% over previous model baseline.
May 12, 2024
