000 03637nam a22005655i 4500
001 978-3-319-51448-2
003 DE-He213
005 20220801213709.0
007 cr nn 008mamaa
008 170420s2017 sz | s |||| 0|eng d
020 _a9783319514482
_9978-3-319-51448-2
024 7 _a10.1007/978-3-319-51448-2
_2doi
050 4 _aR856-857
072 7 _aMQW
_2bicssc
072 7 _aTEC059000
_2bisacsh
072 7 _aMQW
_2thema
082 0 4 _a610.28
_223
100 1 _aMahjoubfar, Ata.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_933740
245 1 0 _aArtificial Intelligence in Label-free Microscopy
_h[electronic resource] :
_bBiological Cell Classification by Time Stretch /
_cby Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXXIII, 134 p. 52 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Background -- Nanometer-resolved imaging vibrometer -- Three-dimensional ultrafast laser scanner -- Label-free High-throughput Phenotypic Screening -- Time Stretch Quantitative Phase Imaging -- Big data acquisition and processing in real-time -- Deep Learning and Classification -- Optical Data Compression in Time Stretch Imaging -- Design of Warped Stretch Transform -- Concluding Remarks and Future Work -- References.
520 _aThis book introduces time-stretch quantitative phase imaging (TS-QPI), a high-throughput label-free imaging flow cytometer developed for big data acquisition and analysis in phenotypic screening. TS-QPI is able to capture quantitative optical phase and intensity images simultaneously, enabling high-content cell analysis, cancer diagnostics, personalized genomics, and drug development. The authors also demonstrate a complete machine learning pipeline that performs optical phase measurement, image processing, feature extraction, and classification, enabling high-throughput quantitative imaging that achieves record high accuracy in label -free cellular phenotypic screening and opens up a new path to data-driven diagnosis. • Demonstrates how machine learning is used in high-speed microscopy imaging to facilitate medical diagnosis; • Provides a systematic and comprehensive illustration of time stretch technology; • Enables multidisciplinary application, including industrial, biomedical, and artificial intelligence.
650 0 _aBiomedical engineering.
_93292
650 0 _aElectronics.
_93425
650 0 _aComputer vision.
_933741
650 0 _aBioinformatics.
_99561
650 1 4 _aBiomedical Engineering and Bioengineering.
_931842
650 2 4 _aElectronics and Microelectronics, Instrumentation.
_932249
650 2 4 _aComputer Vision.
_933742
650 2 4 _aBioinformatics.
_99561
700 1 _aChen, Claire Lifan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_933743
700 1 _aJalali, Bahram.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_933744
710 2 _aSpringerLink (Online service)
_933745
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319514475
776 0 8 _iPrinted edition:
_z9783319514499
776 0 8 _iPrinted edition:
_z9783319846545
856 4 0 _uhttps://doi.org/10.1007/978-3-319-51448-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c75492
_d75492