Titlebook: Artificial Intelligence in Label-free Microscopy; Biological Cell Clas Ata Mahjoubfar,Claire Lifan Chen,Bahram Jalali Book 2017 Springer In

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G. Leimenstoll,M. Schommer,W. Niedermayerents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction an

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K. Haag,U. Blum,S. Baumann,P. Griesta acquisition scheme that enables interruptionless storage of Coherent-STEAM cell images. Our proof of principle demonstration is capable of saving 10.8?TB of cell images in an hour, i.e., pictures of every single cell in 2.7?mL of a sample.

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K. Haag,U. Blum,S. Baumann,P. Griestures of individual cells. In this chapter, we use these biophysical measurements to form a hyperdimensional feature space in which supervised learning is performed for cell classification. We show that TS-QPI not only overcomes the throughput issue in cellular imaging, but also improves label-free

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https://doi.org/10.1007/978-3-642-78697-6 rare cancer cells in blood with record throughput and specificity. An unintended consequence of high-throughput image acquisition is the massive amount of digital data generated by the instrument. Here we report the first experimental demonstration of real-time optical image compression applied to

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https://doi.org/10.1007/978-3-642-78697-6ruments ranging from analog-to-digital converters to cameras and single-shot rare-phenomena capture equipment with record performance have been empowered by it. Its warped stretch variant, realized with nonlinear group delay dispersion, offers variable-rate spectral domain sampling, as well as the a

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Big Data Acquisition and Processing in Real-Timeta acquisition scheme that enables interruptionless storage of Coherent-STEAM cell images. Our proof of principle demonstration is capable of saving 10.8?TB of cell images in an hour, i.e., pictures of every single cell in 2.7?mL of a sample.

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Ata Mahjoubfar,Claire Lifan Chen,Bahram JalaliDemonstrates how machine learning is used in high-speed microscopy imaging to facilitate medical diagnosis;.Provides a systematic and comprehensive illustration of time stretch technology;.Enables mul

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https://doi.org/10.1007/978-3-319-51448-2silicon photonics; real-time instruments for biomedical applications; High-throughput multivariate sen