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Generative adversarial networks (GANs) can fail in medical image generation. Analyzing training loss helps identify the optimal stopping point to ensure meaningful image generation for data augmentation.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Generative adversarial networks (GANs) are widely used for medical image generation and data augmentation.
  • GAN training can result in failure to produce meaningful images, a rarely discussed issue.
  • This failure can introduce bias in medical AI model training and requires significant computational resources.

Purpose of the Study:

  • To investigate the failure modes of deep convolutional GANs in chest X-ray generation.
  • To propose a method for identifying the optimal training termination point to ensure meaningful image generation.
  • To address the challenge of GANs producing unusable images for medical data augmentation.

Main Methods:

  • Utilized deep convolutional GANs for chest X-ray image generation.
  • Analyzed training loss history to identify patterns associated with successful and failed image generation.
  • Developed a method based on the slope of the regression line of stable losses to determine optimal training termination.

Main Results:

  • Identified three typical training outcomes: two successful and one failed generation scenario.
  • Observed that the regression line of overall losses tends to diverge slowly in failed scenarios.
  • Determined that the slope of the regression line within the stable loss segment reliably indicates the optimal training termination point.

Conclusions:

  • The proposed method effectively identifies the optimal training point for GANs in medical image generation.
  • This approach mitigates the risk of generating biased or unusable images for data augmentation.
  • Optimizing GAN training termination is crucial for reliable medical image synthesis.