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: | Danni Chen |
Institution: | UCLA |
Department: | computer science |
Country: | |
Proposed Analysis: | We are students at UCLA who is working on medical imaging project for course CS 168 - Computational Methods for Medical Imaging. We would like to get the access of this dataset so that we can first understand the cause of Alzheimer’s, what are some of the existing and potential computational methods for early detection and treatment. We would also want to help develop explainable AI for diagnosis. Our steps for analyzing the dataset are as follows. 1. We want to explore the dataset by clustering similar images in order to find potential novel characteristic of Alzheimer disease 2. Utilize deep learning frameworks for disease prediction. 3. We would also want to further explore the prediction by using attention model to understand what triggers the model to classify positive diagnose. 4. Using the attention model we would hope we can understand what makes models "believe" patient is having Alzheimer disease. |
Additional Investigators | |
Investigator's Name: | Yuxin Wang |
Proposed Analysis: | We are students at UCLA who is working on medical imaging project for course CS 168 - Computational Methods for Medical Imaging. We would like to get the access of this dataset so that we can first understand the cause of Alzheimer’s, what are some of the existing and potential computational methods for early detection and treatment. We would also want to help develop explainable AI for diagnosis. Our steps for analyzing the dataset are as follows. 1. We want to explore the dataset by clustering similar images in order to find potential novel characteristic of Alzheimer disease 2. Utilize deep learning frameworks for disease prediction. 3. We would also want to further explore the prediction by using attention model to understand what triggers the model to classify positive diagnose. 4. Using the attention model we would hope we can understand what makes models "believe" patient is having Alzheimer disease. |