Adversarial Contrastive Domain-Generative Learning for Bacteria Raman Spectrum Joint Denoising and Cross-Domain Identification
This addresses the challenge of cross-domain identification for pathogenic bacteria in clinical diagnosis, offering a practical tool for real-world applications, though it appears incremental as it builds on existing adversarial and contrastive learning methods.
The paper tackles the problem of identifying bacteria from Raman spectra under varying acquisition conditions by proposing an adversarial contrastive domain-generative learning framework for joint denoising and cross-domain identification, achieving improved diagnostic accuracy and robustness without needing noise-free ground-truth data.
Raman spectroscopy, as a label-free detection technology, has been widely utilized in the clinical diagnosis of pathogenic bacteria. However, Raman signals are naturally weak and sensitive to the condition of the acquisition process. The characteristic spectra of a bacteria can manifest varying signal-to-noise ratios and domain discrepancies under different acquisition conditions. Consequently, existing methods often face challenges when making identification for unobserved acquisition conditions, i.e., the testing acquisition conditions are unavailable during model training. In this article, a generic framework, namely, an adversarial contrastive domain-generative learning framework, is proposed for joint Raman spectroscopy denoising and cross-domain identification. The proposed method is composed of a domain generation module and a domain task module. Through adversarial learning between these two modules, it utilizes only a single available source domain spectral data to generate extended denoised domains that are semantically consistent with the source domain and extracts domain-invariant representations. Comprehensive case studies indicate that the proposed method can simultaneously conduct spectral denoising without necessitating noise-free ground-truth and can achieve improved diagnostic accuracy and robustness under cross-domain unseen spectral acquisition conditions. This suggests that the proposed method holds remarkable potential as a diagnostic tool in real clinical cases.