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1 Ergebnisse
1
Machine Learning Enabled Photoacoustic Spectroscopy for Non..:
Jackson Rodrigues
;
Ashwini Amin
;
Subhash Chandra
...
doi:10.1021/acssensors.3c01085.s001. , 2024
Link:
https://doi.org/10.1021/acssensors.3c01085.s001
RT Journal T1
Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor Progression In Vivo : A Preclinical Study
UL https://suche.suub.uni-bremen.de/peid=base-ftgriffithunifig:oai:figshare.com:article_25111280&Exemplar=1&LAN=DE A1 Jackson Rodrigues A1 Ashwini Amin A1 Subhash Chandra A1 Nitufa J. Mulla A1 G. Subramanya Nayak A1 Sharada Rai A1 Satadru Ray A1 Krishna Kishore Mahato YR 2024 K1 Medicine K1 Molecular Biology K1 Biotechnology K1 Cancer K1 Space Science K1 Biological Sciences not elsewhere classified K1 Chemical Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 input feature matrix K1 7 cells injection K1 detecting breast tumors K1 recorded photoacoustic spectra K1 performing pa experiments K1 corresponding pa spectra K1 different time points K1 biochemical changes associated K1 breast tumor progression K1 time points K1 biochemical changes K1 breast tumor K1 tumor progression K1 photoacoustic spectroscopy K1 transporting tumor K1 pa signal K1 vivo </ K1 subsequently preprocessed K1 results suggest K1 relied upon K1 related deaths K1 preclinical model K1 pas ) K1 nude mice K1 noninvasive assessment K1 maintaining sterility K1 machine learning K1 great need K1 early diagnosis K1 dual purpose K1 device served K1 day 0 K1 current study K1 based classification K1 animal house K1 > K1 established JF doi:10.1021/acssensors.3c01085.s001 LK http://dx.doi.org/https://doi.org/10.1021/acssensors.3c01085.s001 DO https://doi.org/10.1021/acssensors.3c01085.s001 SF ELIB - SuUB Bremen
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