Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
On the Manifold Structure of the Space of Brain Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Combining morphological information in a manifold learning framework: application to neonatal MRI
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Manifold learning for image-based breathing gating with application to 4D ultrasound
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
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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.