There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Feroz Ahmad |
| Institution: | FAST NUCES |
| Department: | ELECTRICAL ENGINEERING |
| Country: | |
| Proposed Analysis: | My research aims to develop an innovative approach for detecting anomalies in medical images, specifically in the context of Alzheimer's disease. By leveraging the power of Variational Autoencoders, I intend to learn the underlying distribution of normal brain images from the ADNI dataset and identify deviations from this learned distribution as anomalies. This research has the potential to contribute to early diagnosis and improved treatment planning for Alzheimer's disease, ultimately benefiting patients and healthcare providers. The ADNI dataset is unique in its comprehensive collection of multimodal neuroimaging data, including structural MRI, functional MRI, PET scans, and genetic data. This richness and diversity make the ADNI dataset an ideal resource for training and evaluating the performance of VAE-based anomaly detection models. By using the ADNI dataset, I will be able to develop a robust and reliable anomaly detection framework that can handle the complexities and variations present in medical images of Alzheimer's disease |
| Additional Investigators |
