… modality situation. In this work, we present a correlation model to represent a latent
correlated relationship between features of each modality. Our idea is to use deep learning to …

… representation. Finally, the correlation representations across modalities are fused via the
attention mechanism into a shared representation to emphasize the most important features …

… knee images from two MRI modalities, provided by … correlated features, since these
crossmodality correlations need to be computed on data that was not used to learn the representation…

… the fact that the various input modalities are highly correlated. We also show that by … ,
and refer to our approach as multimodal (which in the context of MRI is seen as also multi-…

… As an example of the cross-modality analysis, correlation … ), and other imaging modalities
not represented here (such as … -MRI correlation vector represents the vector of correlations of a …

… In the last stage, we use SyN with local cross-correlation, which is demonstrated to work …
In this study, we have adopted datasets which represent typical training data sizes in medical …

… correlation between X and Y , ignoring the relationship between the subjects xi and xj, or
yi and yjfi. The weight matrix of the sparse representation … The VBM-MRI and FDG-PET data …

… Here we present only a small number of representative slices for all findings. A complete set
… most plausible for those correlations involving NAA, which is the MRI modality in our dataset …

… This observation motivated our development of an improved modality-specific cost
functional which uses a weighted local Pearson coefficient (LPC) to align T2⁎- and T1-weighted …

… A total of 202 subjects in the ADNI (202-ADNI) with MRI, PET and CSF modalities are
used in the study, which includes 50 AD subjects, 53NCs, and 99 MCI subjects. The 99 MCI …