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An automatic visual inspection method based on supervised machine learning for rapid on-site evaluation in EUS-FNA

contributor authorInoue, H.
contributor authorOgo, K.
contributor authorTabuchi, M.
contributor authorYamane, N.
contributor authorOka, H.
date accessioned2020-03-12T21:20:31Z
date available2020-03-12T21:20:31Z
date issued2014
identifier other6935253.pdf
identifier urihttp://libsearch.um.ac.ir:80/fum/handle/fum/1045508?show=full
formatgeneral
languageEnglish
publisherIEEE
titleAn automatic visual inspection method based on supervised machine learning for rapid on-site evaluation in EUS-FNA
typeConference Paper
contenttypeMetadata Only
identifier padid8174092
subject keywordsArrays
subject keywordsCovariance matrices
subject keywordsDirection-of-arrival estimation
subject keywordsEstimation
subject keywordsSensors
subject keywordsSignal processing algorithms
subject keywordsSignal to noise ratio
subject keywordsDirection-of-Arrival (DOA)
subject keywordsMUSIC
subject keywordscovariance matrix
subject keywordsunderdetermined mixtures
identifier doi10.1109/CISP.2014.7003923
journal titleICE Annual Conference (SICE), 2014 Proceedings of the
filesize1962530
citations0


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