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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Local Linearity Analysis of Deep Learning CT Denoising Algorithms.

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We developed a local linearity analysis to understand complex neural networks in medical imaging. This method reveals how deep learning models handle non-linear data, showing denoising depends on background intensity.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Deep Learning Analysis
  • Computational Neuroscience

Background:

  • Deep learning models are increasingly used in medical imaging, but their non-linear and data-dependent nature poses analysis challenges.
  • Existing analysis methods struggle to capture the complex behaviors of neural networks, particularly those with piecewise linear components like Rectified Linear Unit (ReLU) activations.

Purpose of the Study:

  • To develop and apply a local linearity analysis framework for understanding non-linear, data-dependent deep learning algorithms in medical imaging.
  • To investigate the piecewise linear nature of neural networks by analyzing locally linear regions and their extensions.

Main Methods:

  • Representing neural networks as piecewise linear systems by analyzing alternating linear layers and ReLU activations.
  • Investigating local linearity by applying perturbations to operating points based on image features (lesion contrast, background, noise).
  • Extending strictly local linear regions to include neighboring regions with similar gradients and applying Singular Value Decomposition (SVD) analysis.

Main Results:

  • Identified a large number of sensitive, strictly local linear regions (33992 over a specific lesion contrast range).
  • Demonstrated that Jacobians are shift-variant but neighboring regions exhibit similar Jacobians, allowing reduction to four approximate linear regions.
  • SVD analysis revealed that CT denoising behavior is highly dependent on background intensity, with more noise reduction in uniform regions than at lesion edges.

Conclusions:

  • Local linearity analysis provides a powerful framework for characterizing and interpreting the non-linear and data-dependent behaviors of deep learning models in medical imaging.
  • The proposed method offers insights into how specific image features, such as background intensity, influence the performance of medical image analysis algorithms.