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Influence on Sample Determination for Deep Learning Electromagnetic Tomography.

Pengfei Zhao1, Ze Liu1

  • 1The School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
Summary
This summary is machine-generated.

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Optimizing sample sets for deep learning electromagnetic tomography (DL-EMT) is crucial. A new CC-building method improves image reconstruction quality, especially with limited data, showing diminishing returns from excessive samples.

Area of Science:

  • * Electrical Engineering
  • * Computational Imaging

Background:

  • * Deep learning (DL) shows promise for enhancing electromagnetic tomography (EMT) image reconstruction.
  • * The impact of sample set size and configuration on DL-EMT model performance is under-researched.
  • * Samples are fundamental for training DL models, yet their optimization is often overlooked.

Purpose of the Study:

  • * To investigate the effect of training set size on DL-EMT reconstruction quality.
  • * To propose and validate a novel sample set optimization method (CC-building) for DL-EMT.
  • * To establish an effective sample base for improved DL-EMT image reconstruction.

Main Methods:

  • * Development of a nine-element deep learning electromagnetic tomography (DL-EMT) model.
  • * Generation of comprehensive simulation and experimental sample datasets.
Keywords:
deep learningelectromagnetic tomographyimage reconstructionsample determination

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  • * Analysis of reconstruction quality with varying training set sizes using Mann-Whitney U tests.
  • * Implementation and experimental validation of the Pearson correlation coefficient-based CC-building method.

    • Main Results:

    • * Statistical analysis indicates that increasing training data beyond a certain threshold yields no significant improvement in DL-EMT image reconstruction quality.
    • * The proposed CC-building method significantly enhances image reconstruction performance, particularly with small to moderate sample sizes.
    • * Experimental validation confirms the efficacy of the CC-building method.

    Conclusions:

    • * Sample set optimization is critical for efficient and effective DL-EMT model training.
    • * The CC-building method offers a statistically sound approach to creating optimized sample sets for DL-EMT.
    • * This method improves reconstruction quality without requiring excessively large datasets, offering practical benefits.