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Abstract:
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This work is motivated by breast cancer imaging data produced by a multimodality multiphoton optical imaging technique. One unique aspect of breast cancer imaging is that different individuals might have breast imaging at different locations, which also creates a technical difficulty in that the imaging background could vary for different individuals. We develop an innovative multilayer tensor learning method to predict disease status effectively through utilizing subject-wise imaging information. In particular, we construct an individualized multilayer model which leverages an additional layer ofcindividual structure of imaging in addition to employing a high-order tensor decomposition shared by populations. In addition, to incorporate multimodality imaging data for different profiling of tissue, cellular and molecular levels, we propose a higher order tensor representation to combine multiple sources of information at different modalities, so important features associated with disease status and clinical outcomes can be extracted effectively. One major advantage of our approach is that we are able to capture the spatial information of microvesicles observed in certain modalities
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