Hu Jiaqi, Xue Chenlong, Chi Ken Xiaokeng, Wei Junyu, Su Zhicheng, Chen Qiuyue, Ou Ziyu, Chen Shuxin, Huang Zhe, Xu Yilin, Wei Haoyun, Liu Yanjun, Shum Perry Ping, Chen Gina Jinna
State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China.
Department of Nephrology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350009, China.
Anal Chem. 2025 Jan 14;97(1):130-139. doi: 10.1021/acs.analchem.4c03261. Epub 2024 Dec 20.
Raman spectroscopy enables label-free clinical diagnosis in a single step. However, identifying an individual carrying a specific disease from people with a multi-disease background is challenging. To address this, we developed a Raman spectral implicit feature augmentation with a Raman Intersection, Union, and Subtraction augmentation strategy (RIUS). RIUS expands the data set without requiring additional labeled data by leveraging set operations at the feature level, significantly enhancing model performance across various applications. On a challenging 30-class bacterial classification task, RIUS demonstrated a substantial improvement, increasing the accuracy of ResNet by 2.1% and that of SE-ResNet by 1.4%, achieving accuracies of 85.7% and 87.1%, respectively, on the Bacteria-ID-4 Data set, where RIUS improved ResNet and SE-ResNet accuracies by 13.6% and 14.5%, respectively, with only ten samples per category. When the sample size was reduced, accuracy gains increased to 31.7% and 38.3%, demonstrating the method's robustness across different sample volumes. Compared to basic augmentation, our method exhibited superior performance across various sample volumes and demonstrated exceptional adaptability to different levels of complexity. RIUS exhibited superior performance, particularly in complex settings. Moreover, cluster analysis validated the effectiveness of the implicit feature augmentation module and the consistency between theoretical design and experimental results. We further validated our approach using clinical serum samples from 70 breast cancer patients and 70 controls, achieving an AUC of 0.94 and a sensitivity of 92.9%. Our approach enhances the potential for precisely identifying diseases in complex settings and offers plug-and-play enhancement for existing classification models.
拉曼光谱能够一步实现无标记临床诊断。然而，从具有多种疾病背景的人群中识别出患有特定疾病的个体具有挑战性。为了解决这个问题，我们开发了一种具有拉曼交集、并集和减法增强策略（RIUS）的拉曼光谱隐式特征增强方法。RIUS通过在特征级别利用集合操作来扩展数据集，而无需额外的标记数据，显著提高了各种应用中的模型性能。在一项具有挑战性的30类细菌分类任务中，RIUS表现出显著的改进，将ResNet的准确率提高了2.1%，将SE-ResNet的准确率提高了1.4%，在Bacteria-ID-4数据集上分别达到了85.7%和87.1%的准确率，其中RIUS在每类仅十个样本的情况下，分别将ResNet和SE-ResNet的准确率提高了13.6%和14.5%。当样本量减少时，准确率提升分别增至31.7%和38.3%，证明了该方法在不同样本量下的稳健性。与基本增强方法相比，我们的方法在各种样本量下均表现出卓越的性能，并对不同复杂程度展现出非凡的适应性。RIUS表现出卓越的性能，尤其是在复杂环境中。此外，聚类分析验证了隐式特征增强模块的有效性以及理论设计与实验结果之间的一致性。我们使用70名乳腺癌患者和70名对照的临床血清样本进一步验证了我们的方法，获得了0.94的AUC和92.9%的灵敏度。我们的方法增强了在复杂环境中精确识别疾病的潜力，并为现有分类模型提供了即插即用的增强功能。
