面向标注高效细菌拉曼光谱聚类与分类的自校准双对比方法
Self-Calibrated Dual Contrasting for Annotation-Efficient Bacteria Raman Spectroscopy Clustering and Classification
AI总结：
本文提出自校准双对比（SCDC）方法，通过嵌入与类别双视角对比学习和自校准机制，在极少或无标注条件下实现稳健的细菌拉曼光谱聚类与分类。
摘要：
拉曼散射基于分子振动光谱，利用物质独特的分子指纹信息，为病原菌诊断提供了强大的技术手段。深度学习技术的引入显著提升了智能拉曼光谱（RS）识别的效率和准确性。然而，当前基于深度神经网络的RS识别方法仍需要标注大量光谱数据，这一过程耗费大量人力。本文提出了一种新颖的标注高效自校准双对比（SCDC）方法用于RS识别，该方法在极少标注或无标注条件下即可有效运行。我们的核心动机是在两个不同的子空间——嵌入子空间和类别子空间——中从两个不同视角表示光谱。嵌入视角捕获实例级信息，而类别视角反映类别级信息。据此，我们从这两个视角实现了双对比学习方法，以获得具有判别力的表示，这些表示适用于无监督和半监督学习条件下的拉曼光谱识别。此外，还提出了一种自校准机制以增强鲁棒性。在三个大规模细菌拉曼光谱数据集上的识别任务验证表明，我们的SCDC方法在仅有极少量（5%或10%）标注或无标注的情况下仍能实现稳健的识别性能，凸显了所提方法在标注高效的临床场景中进行生物光谱识别的潜力。
英文摘要：
Raman scattering is based on molecular vibration spectroscopy and provides a powerful technology for pathogenic bacteria diagnosis using the unique molecular fingerprint information of a substance. The integration of deep learning technology has significantly improved the efficiency and accuracy of intelligent Raman spectroscopy (RS) recognition. However, the current RS recognition methods based on deep neural networks still require the annotation of a large amount of spectral data, which is labor-intensive. This paper presents a novel annotation-efficient Self-Calibrated Dual Contrasting (SCDC) method for RS recognition that operates effectively with few or no annotation. Our core motivation is to represent the spectrum from two different perspectives in two distinct subspaces: embedding and category. The embedding perspective captures instance-level information, while the category perspective reflects category-level information. Accordingly, we have implemented a dual contrastive learning approach from two perspectives to obtain discriminative representations, which are applicable for Raman spectroscopy recognition under both unsupervised and semi-supervised learning conditions. Furthermore, a self-calibration mechanism is proposed to enhance robustness. Validation of the identification task on three large-scale bacterial Raman spectroscopy datasets demonstrates that our SCDC method achieves robust recognition performance with very few (5$\%$ or 10$\%$) or no annotations, highlighting the potential of the proposed method for biospectral identification in annotation-efficient clinical scenarios.
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。
