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Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia
Katherine Gray, Paul Aljabar, R. A. Heckemann, A. Hammers, Daniel Rueckert
- Conference or Workshop Paper
- MLMI Workshop, MICCAI 2011
- 2011
- Lecture Notes in Computer Science
- Volume 7009
- pp.159–166
- Springer
- DOI 10.1007/978-3-642-24319-6_20
- Abstract
Neurodegenerative disorders are characterized by changes in multiple biomarkers, which may provide complementary information for diagnosis and prognosis. We present a framework in which proximities derived from random forests are used to learn a low-dimensional manifold from labelled training data and then to infer the clinical labels of test data mapped to this space. The proposed method facilitates the combination of embeddings from multiple datasets, resulting in the generation of a joint embedding that simultaneously encodes information about all the available features. It is possible to combine different types of data without additional processing, and we demonstrate this key feature by application to voxel-based FDG-PET and region-based MR imaging data from the ADNI study. Classification based on the joint embedding coordinates out-performs classification based on either modality alone. Results are impressive compared with other state-of-the-art machine learning techniques applied to multi-modality imaging data.
- Notes
Winner of the best paper award for the workshop
BibTEX file for the publication
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