ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024
HIGHER ORDER GAUGE EQUIVARIANT CONVOLUTIONS FOR NEURODEGENERATIVE DISORDER CLASSIFICATION.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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6 authors.
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Abstract
Diffusion MRI (dMRI) has shown significant promise in capturing subtle changes in neural microstructure caused by neurodegenerative disorders. In this paper, we propose a novel end-to-end compound architecture for processing raw dMRI data. It consists of a 3D convolutional kernel network (CKN) that extracts macro-architectural features across voxels and a gauge equivariant Volterra network (GEVNet) on the sphere that extracts micro-architectural features from within voxels. The use of higher order convolutions enables our architecture to model spatially extended nonlinear interactions across the applied diffusion-sensitizing magnetic field gradients. The compound network is globally equivariant to 3D translations and locally equivariant to 3D rotations. We demonstrate the efficacy of our model on the classification of neurodegenerative disorders.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.