Ghost cytometry

Science

Volumn 360, Issue 6394, 2018, Pages 1246-1251

  Ota, Sadao ^a,b,c   Horisaki, Ryoichi ^c,d   Kawamura, Yoko ^a,b   Ugawa, Masashi ^a   Sato, Issei ^a,b,c,e   Hashimoto, Kazuki ^b,f   Kamesawa, Ryosuke ^a,b   Setoyama, Kotaro ^a   Yamaguchi, Satoko ^b   Fujiu, Katsuhito ^b   Waki, Kayo ^b   Noji, Hiroyuki ^b,g  

^a ThinkCyte   (Japan)

^b UNIVERSITY OF TOKYO   (Japan)

^c JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^d OSAKA UNIVERSITY   (Japan)

^e Disaster Resilience Science Team   (Japan)

^f JAPAN AEROSPACE EXPLORATION AGENCY   (Japan)

^g Cabinet Office   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FLOW CYTOMETRY; IMAGE; INSTRUMENTATION; MACHINE LEARNING; PIXEL;

ARTICLE; CELL SELECTION; CELL STRUCTURE; CLASSIFIER; CYTOMETRY; FLUORESCENCE IMAGING; GHOST CYTOMETRY; HUMAN; IMAGE RECONSTRUCTION; LIVE CELL IMAGING; MACHINE LEARNING; MCF-7 CELL LINE; MIA PACA-2 CELL LINE; MICROFLUIDICS; PRIORITY JOURNAL; SIGNAL NOISE RATIO; SUPPORT VECTOR MACHINE; CELL SEPARATION; CELLS; CLASSIFICATION; CYTOLOGY; FLOW CYTOMETRY; IMAGE CYTOMETRY; PROCEDURES; SINGLE CELL ANALYSIS;

CELL SEPARATION; CELLS; FLOW CYTOMETRY; HUMANS; IMAGE CYTOMETRY; MACHINE LEARNING; MCF-7 CELLS; SINGLE-CELL ANALYSIS;

EID: 85053299658     PISSN: 00368075     EISSN: 10959203     Source Type: Journal    
DOI: 10.1126/science.aan0096     Document Type: Article

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