biostudies-literatureZhang YNIBIB NIH HHSHoward Hughes Medical InstituteNational Science Foundation91https://www.ebi.ac.uk/biostudies/studies/S-EPMC6804677biostudies-literatureUnknown8Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automaticLight, science & applicationsComputational cytometer based on magnetically modulated coherent imaging and deep learning.PMC6804677R21 EB023115Kim DBai BYalcin SCheung CTsai KLiu TKong JOuyang MDuan ZLiu XFeizi AZhang YCeylan Koydemir HGuziak ADi Carlo DRay ALuo YOzcan AGarner OBfalseComputational cytometer based on magnetically modulated coherent imaging and deep learning.Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatic2019-01-01T00:00:00Z20192026-05-07T03:48:41.417Z2019-11-05T08:09:27ZS-EPMC68046773164593510.1038/s41377-019-0203-5