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- Title
Deep Kronecker network.
- Authors
Feng, Long; Yang, Guang
- Abstract
We develop a novel framework for the analysis of medical imaging data, including magnetic resonance imaging, functional magnetic resonance imaging, computed tomography and more. Medical imaging data differ from general images in two main aspects: (i) the sample size is often considerably smaller and (ii) the interpretation of the model is usually more crucial than predicting the outcome. As a result, standard methods such as convolutional neural networks cannot be directly applied to medical imaging analysis. Therefore, we propose the deep Kronecker network , which can adapt to the low sample size constraint and offer the desired model interpretation. Our approach is versatile, as it works for both matrix- and tensor-represented image data and can be applied to discrete and continuous outcomes. The deep Kronecker network is built upon a Kronecker product structure, which implicitly enforces a piecewise smooth property on coefficients. Moreover, our approach resembles a fully convolutional network as the Kronecker structure can be expressed in a convolutional form. Interestingly, our approach also has strong connections to the tensor regression framework proposed by Zhou et al. (2013) , which imposes a canonical low-rank structure on tensor coefficients. We conduct both classification and regression analyses using real magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate the effectiveness of our approach.
- Subjects
MAGNETIC resonance imaging; KRONECKER products; IMAGE analysis; DIAGNOSTIC imaging; CONVOLUTIONAL neural networks; FUNCTIONAL magnetic resonance imaging
- Publication
Biometrika, 2024, Vol 111, Issue 2, p707
- ISSN
0006-3444
- Publication type
Article
- DOI
10.1093/biomet/asad049