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Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains.

Ronak Mehta1, Rudrasis Chakraborty2, Yunyang Xiong1

  • 1University of Wisconsin Madison.

Proceedings. IEEE International Conference on Computer Vision
|March 16, 2022
PubMed
Summary

This study introduces an orthogonal tensor train method to efficiently analyze 3D medical image sequences, enabling powerful recurrent neural networks for whole brain analysis and improving diagnostic reliability.

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Last Updated: Sep 30, 2025

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Area of Science:

  • Medical Imaging
  • Deep Learning
  • Neuroscience

Background:

  • Deep networks excel in real-world image analysis but struggle with large 3D medical images.
  • Analyzing longitudinal 3D image data with recurrent networks is computationally demanding.
  • Point estimates in scientific applications require reliability measures.

Purpose of the Study:

  • To adapt tensor train decomposition for efficient deep network construction in medical imaging.
  • To develop a recurrent network capable of analyzing whole brain image volume sequences.
  • To improve parameter efficiency and reliability measures in deep learning models for 3D medical data.

Main Methods:

  • Utilizing differential geometry insights to adapt tensor train decomposition.
  • Developing an "orthogonal" tensor train for constructing neural networks with fewer parameters.
  • Implementing recurrent networks for longitudinal analysis of 3D brain image volumes.

Main Results:

  • The orthogonal tensor train significantly reduces network parameters compared to standard methods.
  • Faster convergence and stronger confidence intervals were achieved in reconstructing whole brain volumes.
  • The model demonstrated effectiveness in regressing cognition-related outcomes from image sequences.

Conclusions:

  • Orthogonal tensor trains offer a computationally efficient approach for deep learning in 3D medical imaging.
  • This method enables powerful recurrent analysis of longitudinal brain data.
  • The approach enhances reliability and accuracy for neuroimaging applications.