TY - GEN
T1 - Deep-learning-enabled virtual immunofluorescence staining based on reflectance microscopy
AU - Cheng, Shiyi
AU - Fu, Sipei
AU - Kim, Yumi Mun
AU - Yi, Ji
AU - Tian, Lei
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - To circumvent the limitations of conventional immunofluorescence (IF) microscopy, a deep learning approach is proposed for transforming morphological information contained in reflectance microscopy to specific and accurate IF prediction with high multiplexing capability.
AB - To circumvent the limitations of conventional immunofluorescence (IF) microscopy, a deep learning approach is proposed for transforming morphological information contained in reflectance microscopy to specific and accurate IF prediction with high multiplexing capability.
UR - http://www.scopus.com/inward/record.url?scp=85097903286&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097903286&partnerID=8YFLogxK
U2 - 10.1109/IPC47351.2020.9252556
DO - 10.1109/IPC47351.2020.9252556
M3 - Conference contribution
AN - SCOPUS:85097903286
T3 - 2020 IEEE Photonics Conference, IPC 2020 - Proceedings
BT - 2020 IEEE Photonics Conference, IPC 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Photonics Conference, IPC 2020
Y2 - 28 September 2020 through 1 October 2020
ER -