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Large datasets often contain multiple distinct feature sets, or views, thatoffer complementary information that can be exploited by multi-view learningmethods to improve results. We investigate anatomical multi-view data, whereeach brain anatomical structure is described with multiple feature sets. Inparticular, we focus on sets of white matter microstructure and connectivityfeatures from diffusion MRI, as well as sets of gray matter area and thicknessfeatures from structural MRI. We investigate machine learning methodology thatapplies multi-view approaches to improve the prediction of non-imagingphenotypes, 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. Inthis network, each individual anatomical view is processed by a view-specificfeature extractor and the extracted information from each view is fused using alearnable weight. This is followed by a wavelet transform-based module toobtain complementary information across views which is then applied tocalibrate the view-specific information. Additionally, the calibrator producesan attention-based calibration score to indicate anatomical structures'importance for interpretation.

A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data