  Large datasets often contain multiple distinct feature sets, or views, that
offer complementary information that can be exploited by multi-view learning
methods to improve results. We investigate anatomical multi-view data, where
each brain anatomical structure is described with multiple feature sets. In
particular, we focus on sets of white matter microstructure and connectivity
features from diffusion MRI, as well as sets of gray matter area and thickness
features from structural MRI. We investigate machine learning methodology that
applies multi-view approaches to improve the prediction of non-imaging
phenotypes, including demographics (age), motor (strength), and cognition
(picture vocabulary). We present an explainable multi-view network (EMV-Net)
that can use different anatomical views to improve prediction performance. In
this network, each individual anatomical view is processed by a view-specific
feature extractor and the extracted information from each view is fused using a
learnable weight. This is followed by a wavelet transform-based module to
obtain complementary information across views which is then applied to
calibrate the view-specific information. Additionally, the calibrator produces
an attention-based calibration score to indicate anatomical structures'
importance for interpretation.
