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: | Mostafa EL HABIB DAHO |
| Institution: | University of Tlemcen |
| Department: | Biomedical Engineering |
| Country: | |
| Proposed Analysis: | Our research work aims to propose a new deep architecture to predict Alzheimer's disease better. This research's ultimate, long-term goal would be to provide the community with better models/tools for predicting the different stages of Alzheimer's disease. We have already started the development of a classification pipeline with new explainable models based on classical architectures (Visual transformers, Contrastive Captioners, EfficientNEt,..). Explainable architectures are crucial to justify the model's decision in the medical field. The ADNI 3 dataset will be of great use to us given its richness and that it is the standard benchmark in detecting Alzheimer's disease. |
| Additional Investigators | |
| Investigator's Name: | Mohammed El Amine LAZOUNI |
| Proposed Analysis: | Our research work aims to propose a new deep architecture to predict Alzheimer's disease better. This research's ultimate, long-term goal would be to provide the community with better models/tools for predicting the different stages of Alzheimer's disease. We have already started the development of a classification pipeline with new explainable models based on classical architectures (Visual transformers, Contrastive Captioners, EfficientNEt,..). Explainable architectures are crucial to justify the model's decision in the medical field. The ADNI 3 dataset will be of great use to us given its richness and that it is the standard benchmark in detecting Alzheimer's disease. |
| Investigator's Name: | Fatima Zahra BENYELLES |
| Proposed Analysis: | Our research work aims to propose a new deep architecture to predict Alzheimer's disease better. This research's ultimate, long-term goal would be to provide the community with better models/tools for predicting the different stages of Alzheimer's disease. We have already started the development of a classification pipeline with new explainable models based on classical architectures (Visual transformers, Contrastive Captioners, EfficientNEt,..). Explainable architectures are crucial to justify the model's decision in the medical field. The ADNI 3 dataset will be of great use to us given its richness and that it is the standard benchmark in detecting Alzheimer's disease. |
