用于细菌拉曼光谱联合去噪与跨域识别的对抗对比域生成学习
Adversarial Contrastive Domain-Generative Learning for Bacteria Raman Spectrum Joint Denoising and Cross-Domain Identification
AI总结：
针对细菌拉曼光谱跨采集条件识别难的问题，提出对抗对比域生成学习框架，仅用单源域数据实现联合去噪与跨域识别，提升了诊断准确率和鲁棒性，具备临床应用潜力。
摘要：
拉曼光谱作为一种无标记检测技术，已被广泛应用于病原菌的临床诊断。然而，拉曼信号本身较为微弱，且对采集过程的条件十分敏感。同一种细菌的特征光谱在不同采集条件下会呈现出不同的信噪比和域差异。因此，现有方法在针对未见过的采集条件（即训练模型时无法获取测试用的采集条件）进行识别时往往面临挑战。本文提出了一种通用框架，即对抗对比域生成学习框架，用于拉曼光谱的联合去噪与跨域识别。该方法由域生成模块和域任务模块组成。通过两个模块之间的对抗学习，它仅利用单一可用的源域光谱数据，就能生成与源域语义一致的扩展去噪域，并提取域不变表征。综合案例研究表明，该方法无需无噪声的真值数据即可同时进行光谱去噪，并且在跨域未见过的光谱采集条件下能提升诊断准确率和鲁棒性。这表明该方法作为实际临床案例中的诊断工具具有显著潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
