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: | Peng peng |
| Institution: | Central South University |
| Department: | School of Computer science and Engineering |
| Country: | |
| Proposed Analysis: | The accurate diagnosis of Alzheimer’s disease (AD) and its early stage, e.g., mild cognitive impairment (MCI), is essential for timely treatment or possible intervention to slow down AD progression.Generically regarding the different neuroimaging modalities as filtered, complementary insights of brain’s anatomical and functional organization, multimodal data fusion could be hypothesized to enhance the predictive power as compared to a unimodal prediction of disease progression. Therefore, information fusion strategies with multi-modal neuroimaging data, such as MRI and fluorodeoxyglucose positron emission tomography (FDG-PET), have shown their effectiveness for AD diagnosis.So we propose a deep learning-based multi-scale and multi-level fusing approach of CNNs for AD diagnosis. We introduce a multi-scale representation strategy to encode both the local and semi-local complementary information of the images. To take advantage of the complementary information of multi-scale representations,we propose a multi-level fusion method to combine the information of both the feature level and the decision level hierarchically and generate a robust diagnostic classifier based on deep learning, thus achieve the early prediction of AD. |
| Additional Investigators |
