TY - GEN
T1 - Predicting immunofluorescence images from reflectance microscopy via deep learning
AU - Cheng, Shiyi
AU - Fu, Sipei
AU - Kim, Yumi Mun
AU - Yi, Ji
AU - Tian, Lei
N1 - Publisher Copyright:
© 2020 The Author(s)
PY - 2020
Y1 - 2020
N2 - To circumvent the limitations of immunofluorescence microscopy, we propose a deep learning approach for characterizing morphological information contained in reflectance microscopy with high specificity and enable digital multiplexing.
AB - To circumvent the limitations of immunofluorescence microscopy, we propose a deep learning approach for characterizing morphological information contained in reflectance microscopy with high specificity and enable digital multiplexing.
UR - http://www.scopus.com/inward/record.url?scp=85091396805&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091396805&partnerID=8YFLogxK
U2 - 10.1364/MICROSCOPY.2020.MTu2A.5
DO - 10.1364/MICROSCOPY.2020.MTu2A.5
M3 - Conference contribution
AN - SCOPUS:85091396805
SN - 9781943580743
T3 - Optics InfoBase Conference Papers
BT - Microscopy Histopathology and Analytics, Microscopy 2020
PB - Optica Publishing Group (formerly OSA)
T2 - Microscopy Histopathology and Analytics, Microscopy 2020
Y2 - 20 April 2020 through 23 April 2020
ER -