DiffRaman: A conditional latent denoising diffusion probabilistic model for enhancing bacterial identification via...

Area of Science:

• Biochemical Detection
• Spectroscopy
• Artificial Intelligence

Background:

• Raman spectroscopy is crucial for rapid pathogenic bacteria identification.
• Deep learning enhances automated bacterial Raman spectroscopy diagnosis.
• Limited data hinders deep learning model performance in spectral analysis.

Purpose of the Study:

• To propose a data generation method using deep generative models for bacterial Raman spectra.
• To expand data volume and improve recognition accuracy in data-scarce scenarios.
• To introduce DiffRaman, a conditional latent denoising diffusion probability model.

Main Methods:

• Applied 2D figure transformation to Raman spectral data.
• Used Vector Quantized Variational Autoencoder (VQ-VAE) for spectral data compression into a latent space.
• Implemented a Conditional Denoising Diffusion Probabilistic Model (DDPM) for representation learning and data augmentation.

Main Results:

• DiffRaman-generated synthetic spectra effectively mimic real bacterial Raman spectra.
• Improved diagnostic model performance, particularly in data-limited settings.
• DiffRaman demonstrated superior generation quality and computational efficiency over existing models.

Conclusions:

• DiffRaman is a viable solution for automated bacteria Raman spectroscopy diagnosis in data-scarce environments.
• The method alleviates challenges associated with limited spectroscopic measurements.
• Enhances the identification of rare bacteria through effective data augmentation.