Difference between revisions of "2011 Summer Project Week Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps"
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Image:PW-MIT2011.png|[[2011_Summer_Project_Week#Projects|Projects List]] | Image:PW-MIT2011.png|[[2011_Summer_Project_Week#Projects|Projects List]] | ||
| + | Image:adni_embedding2.png|Learned embedding from part of ADNI data | ||
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'''Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps''' | '''Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps''' | ||
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#:We want to learn better embeddings of brain images (to better perform classification, segmentation/registration, etc). We will use a modification of spectral embedding techniques that allows us to incorporate constraints. For example, when longitudinal data involving progression of some pathology is available, we would like to incorporate our knowledge about the temporal relationship by constraining the longitudinal images to line up. | #:We want to learn better embeddings of brain images (to better perform classification, segmentation/registration, etc). We will use a modification of spectral embedding techniques that allows us to incorporate constraints. For example, when longitudinal data involving progression of some pathology is available, we would like to incorporate our knowledge about the temporal relationship by constraining the longitudinal images to line up. | ||
| − | #:This is a relatively new project. | + | #:This is a relatively new project, and we are interested in hearing about potential applications and useful directions for the work. |
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| + | #:Applied method to part of the ADNI dataset | ||
| + | #:Obtained HD data for future analysis | ||
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==References== | ==References== | ||
| + | Belkin, M; Niyogi, P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation | ||
==Delivery Mechanism== | ==Delivery Mechanism== | ||
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Latest revision as of 14:10, 24 June 2011
Home < 2011 Summer Project Week Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps
Learned embedding from part of ADNI data
Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps
Key Investigators
- MIT: Ramesh Sridharan
- MIT: Polina Golland
Objective
- Use image manifold learning to better understand pathology (e.g. Alzheimer's, Huntington's) in brain images.
Approach, Plan
- We want to learn better embeddings of brain images (to better perform classification, segmentation/registration, etc). We will use a modification of spectral embedding techniques that allows us to incorporate constraints. For example, when longitudinal data involving progression of some pathology is available, we would like to incorporate our knowledge about the temporal relationship by constraining the longitudinal images to line up.
- This is a relatively new project, and we are interested in hearing about potential applications and useful directions for the work.
Progress
- Applied method to part of the ADNI dataset
- Obtained HD data for future analysis
References
Belkin, M; Niyogi, P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
