Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia

Katherine R. Gray, Paul Aljabar, Rolf A. Heckemann, Alexander Hammers, Daniel Rueckert

Research output: Chapter in Book/Report/Conference proceedingConference paper

21 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMachine learning in medical imaging
EditorsK Suzuki, F Wang, DG Shen, PK Yan
Place of PublicationBERLIN
PublisherSpringer
Pages159-166
Number of pages8
Volume7009 LNCS
ISBN (Print)9783642243189
Publication statusPublished - 2011
Event2nd International Workshop on Machine Learning in Medical Imaging (MLMI 2011) - Toronto
Duration: 18 Sept 2011 → …

Conference

Conference2nd International Workshop on Machine Learning in Medical Imaging (MLMI 2011)
CityToronto
Period18/09/2011 → …

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