{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang Y"],"funding":["NIBIB NIH HHS","Howard Hughes Medical Institute","National Science Foundation"],"pagination":["91"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6804677"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8"],"pubmed_abstract":["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 automatic"],"journal":["Light, science & applications"],"pubmed_title":["Computational cytometer based on magnetically modulated coherent imaging and deep learning."],"pmcid":["PMC6804677"],"funding_grant_id":["R21 EB023115"],"pubmed_authors":["Kim D","Bai B","Yalcin S","Cheung C","Tsai K","Liu T","Kong J","Ouyang M","Duan Z","Liu X","Feizi A","Zhang Y","Ceylan Koydemir H","Guziak A","Di Carlo D","Ray A","Luo Y","Ozcan A","Garner OB"],"additional_accession":[]},"is_claimable":false,"name":"Computational cytometer based on magnetically modulated coherent imaging and deep learning.","description":"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 automatic","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019","modification":"2026-05-07T03:48:41.417Z","creation":"2019-11-05T08:09:27Z"},"accession":"S-EPMC6804677","cross_references":{"pubmed":["31645935"],"doi":["10.1038/s41377-019-0203-5"]}}