Xiaoyong Lan
Biographic Data
| ID | 10014550 |
|---|---|
| NAME | Xiaoyong Lan |
| GIVEN NAMES | Xiaoyong |
| FAMILY NAME | Lan |
| SIGNATURE | LAN X |
| AFFILIATIONS | Jinan University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Alzheimer's Disease Classification With a Cascade Neural Network
Classification of Alzheimer's Disease (AD) has been becoming a hot issue along with the rapidly increasing number of patients. This task remains tremendously challenging due to the limited data and the difficulties in detecting mild cognitive impairment (MCI). Existing methods use gait [or EEG (electroencephalogram)] data only to tackle this task. Although the gait data acquisition procedure is cheap and simple, the methods relying on gait data o…
Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease
While machine learning approaches to analyzing Alzheimer disease connectome neuroimaging data have been studied, many have limited ability to provide insight in individual patterns of disease and lack the ability to provide actionable information about where in the brain a specific patient's disease is located. We studied a cohort of patients with Alzheimer disease who underwent resting state functional magnetic resonance imaging and diffusion tr…
No prominent works on this page.
Alzheimer's Disease Classification With a Cascade Neural Network
Classification of Alzheimer's Disease (AD) has been becoming a hot issue along with the rapidly increasing number of patients. This task remains tremendously challenging due to the limited data and the difficulties in detecting mild cognitive impairment (MCI). Existing methods use gait [or EEG (electroencephalogram)] data only to tackle this task. Although the gait data acquisition procedure is cheap and simple, the methods relying on gait data o…
Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease
While machine learning approaches to analyzing Alzheimer disease connectome neuroimaging data have been studied, many have limited ability to provide insight in individual patterns of disease and lack the ability to provide actionable information about where in the brain a specific patient's disease is located. We studied a cohort of patients with Alzheimer disease who underwent resting state functional magnetic resonance imaging and diffusion tr…
Computer Science (2 works) · Medicine (2 works) · Neuroscience (2 works) · Psychology (2 works) · Advanced MRI Techniques and Applications (1 works) · Advanced Neuroimaging Techniques and Applications (1 works) · Alzheimer's disease (1 works) · Artificial Intelligence (1 works) · Connectome (1 works) · Connectomics (1 works)