Conditional Generative Adversarial Networks for Data Augmentation of a Neonatal Image Dataset.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neonatal Care

Background:

• Current neonatal vital sign monitoring uses sensors that can harm premature infants' skin.
• Camera-based monitoring with deep learning offers a cable-free alternative but requires extensive data.
• Limited neonatal image datasets hinder the validation of deep learning algorithms.

Purpose of the Study:

• To investigate the use of conditional generative adversarial networks (cGANs) for data augmentation in neonatal imaging.
• To create realistic RGB images from edge detection frames of neonates for enlarging training datasets.
• To evaluate the effectiveness of different edge detection methods on cGAN performance.

Main Methods:

• Adapted the Pix2PixHD network architecture for image generation.
• Utilized edge detection frames from neonates as input for the cGAN.
• Optimized hyperparameters and validated generated images using human evaluation and FID score.

Main Results:

• Generated RGB images were evaluated by 30 volunteers, with 23% misidentified as real in a fake-only test.
• In direct comparison, 28% of generated images were rated as more realistic than manually augmented real images.
• Achieved a Frechet Inception Distance (FID) score of 103.82, indicating good image quality.

Conclusions:

• Conditional generative adversarial networks show promise for augmenting limited neonatal image datasets.
• This approach can significantly improve the training and validation of deep learning algorithms for cable-free neonatal monitoring.
• The study demonstrates a viable method to overcome data scarcity in medical AI research for neonates.