首页|Chinese Academy of Sciences Reports Findings in Artificial Intelligence (RamanCl uster: A deep clustering-based framework for unsupervised Raman spectral identif ication of pathogenic bacteria)
Chinese Academy of Sciences Reports Findings in Artificial Intelligence (RamanCl uster: A deep clustering-based framework for unsupervised Raman spectral identif ication of pathogenic bacteria)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Artificial Intelligenc e is the subject of a report. According tonews originating from Shenyang, Peopl e’s Republic of China, by NewsRx correspondents, research stated,“Raman spectro scopy serves as a powerful and reliable tool for the characterization of pathoge nic bacteria. The integration of Raman spectroscopy with artificial intelligence techniques to rapidly identify pathogenicbacteria has become paramount for exp editing disease diagnosis.”Our news journalists obtained a quote from the research from the Chinese Academy of Sciences,“However, the development of prevailing supervised artificial inte lligence algorithms is still constrainedby costly and limited well-annotated Ra man spectroscopy datasets. Furthermore, tackling various highdimensionaland in tricate Raman spectra of pathogenic bacteria in the absence of annotations remai ns aformidable challenge. In this paper, we propose a concise and efficient dee p clustering-based framework(RamanCluster) to achieve accurate and robust unsup ervised Raman spectral identification of pathogenicbacteria without the need fo r any annotated data. RamanCluster is composed of a novel representationlearnin g module and a machine learning-based clustering module, systematically enabling the extraction ofrobust discriminative representations and unsupervised Raman spectral identification of pathogenic bacteria.The extensive experimental resul ts show that RamanCluster has achieved high accuracy on both Bacteria-4and Bact eria-6, with ACC values of 77 % and 74.1 %, NMI value s of 75 % and 73 %, as well as AMIvalues of 74.6 % and 72.6 %, respectively. Furthermore, compared with other state-of -the-art methods,RamanCluster exhibits the superior accuracy on handling variou s complicated pathogenic bacterial Ramanspectroscopy datasets, including situat ions with strong noise and a wide variety of pathogenic bacterialspecies. Addit ionally, RamanCluster also demonstrates commendable robustness in these challeng ingscenarios.”
ShenyangPeople’s Republic of ChinaAsiaArtificial IntelligenceBacteriaEmerging TechnologiesMachine Learning
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