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20 August 2020 Deep learning-based cytometer using magnetically modulated coherent imaging
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We present a high-throughput and cost-effective computational cytometer for rare cell detection, where the target cells are specifically labeled with magnetic particles and exhibit an oscillatory motion under a periodically-changing magnetic field. The time-varying diffraction patterns of the oscillating cells are then captured with a holographic imaging system and are further classified by a customized pseudo-3D convolutional network. To evaluate the performance of our technique, we detected serially-diluted MCF7 cancer cells that were spiked in whole blood, achieving a limit of detection (LoD) of 10 cells per 1 mL of whole blood.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tairan Liu, Yibo Zhang, Mengxing Ouyang, Aniruddha Ray, Janay Kong, Bijie Bai, Donghyuk Kim, Alexander Guziak, Yi Luo, Alborz Feizi, Katherine Tsai, Zhuoran Duan, Xuewei Liu, Danny Kim, Chloe Cheung, Sener Yalcin, Hatice Ceylan Koydemir, Omai B. Garner, Dino Di Carlo, and Aydogan Ozcan "Deep learning-based cytometer using magnetically modulated coherent imaging", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691H (20 August 2020);

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