DiffRaman: A Conditional Latent Denoising Diffusion Probabilistic Model for Bacterial Raman Spectroscopy Identification Under Limited Data Conditions

TL;DR

The paper tackles the challenge of limited Raman spectroscopy data for reliable bacterial identification by introducing DiffRaman, a conditional latent diffusion framework that operates in a compressed latent space via VQ-VAE. By transforming spectra into 2D Raman figures, compressing them to discrete latent codes, and applying a conditioned diffusion process, DiffRaman can generate high-quality, diverse spectra conditioned on bacterial type. This synthetic data, used alongside real measurements in a dual-stream training setup, substantially improves downstream diagnostic accuracy under data-scarce conditions and outperforms several existing generative baselines in both generation quality and efficiency. Evaluations on two large bacterial Raman datasets show that DiffRaman yields spectra with fidelity to real data and enhances identification tasks across multiple benchmarks, suggesting practical utility for reducing labor in spectral collection and aiding clinical decision-making when samples are scarce.

Abstract

Raman spectroscopy has attracted significant attention in various biochemical detection fields, especially in the rapid identification of pathogenic bacteria. The integration of this technology with deep learning to facilitate automated bacterial Raman spectroscopy diagnosis has emerged as a key focus in recent research. However, the diagnostic performance of existing deep learning methods largely depends on a sufficient dataset, and in scenarios where there is a limited availability of Raman spectroscopy data, it is inadequate to fully optimize the numerous parameters of deep neural networks. To address these challenges, this paper proposes a data generation method utilizing deep generative models to expand the data volume and enhance the recognition accuracy of bacterial Raman spectra. Specifically, we introduce DiffRaman, a conditional latent denoising diffusion probability model for Raman spectra generation. Experimental results demonstrate that synthetic bacterial Raman spectra generated by DiffRaman can effectively emulate real experimental spectra, thereby enhancing the performance of diagnostic models, especially under conditions of limited data. Furthermore, compared to existing generative models, the proposed DiffRaman offers improvements in both generation quality and computational efficiency. Our DiffRaman approach offers a well-suited solution for automated bacteria Raman spectroscopy diagnosis in data-scarce scenarios, offering new insights into alleviating the labor of spectroscopic measurements and enhancing rare bacteria identification.

Figures (10)

• Figure 1: The proposed DiffRaman methodology: (a) DiffRaman employs a dual-data-stream approach to train diagnostic models. The first branch consists of real bacterial Raman spectroscopy data collected under actual experimental conditions, while the second branch involves synthetic bacterial Raman spectra generated by DiffRaman. The spectra from both branches are used together to train the diagnostic model, thereby achieving more robust performance.(b) The workflow of DiffRaman primarily encompasses four steps: data transformation, latent compression, conditional generation, and reconstruction. (c) Schematic illustrations of the forward noise adding and reverse denoising diffusion processes.
• Figure 2: (a) Schematic diagram of the proposed transformation method for converting Raman spectra into two-dimensional Raman figures. (b) Examples of Raman spectra and their corresponding converted Raman figures.
• Figure 3: Schematic illustration of forward noise addition and reverse denoising for Raman spectroscopy based on Markov chain.
• Figure 4: The specific network architectures of the encoder, decoder, and UNet components used in DiffRaman.
• Figure 5: A comparative example of real and generated Raman figures. (a)-(d): Isolate-averaged Raman figure for E. faecalis 2, MSSA 3, S. lugdunensis, and Group C Strep. in Bacteria ID dataset. (e)-(h): Species-averaged Raman figure for S. aureus, P. aeruginosa, S. epidermidis, and E. faecalis in Bacteria stains dataset.