Quantitative phase imaging: recent advances and expanding potential in biomedicine
Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with
significant opportunities for biomedical applications. QPI uses the natural phase shift of light …
significant opportunities for biomedical applications. QPI uses the natural phase shift of light …
[HTML][HTML] Artificial intelligence in healthcare: review and prediction case studies
Artificial intelligence (AI) has been develo** rapidly in recent years in terms of software
algorithms, hardware implementation, and applications in a vast number of areas. In this …
algorithms, hardware implementation, and applications in a vast number of areas. In this …
Roadmap on digital holography
This Roadmap article on digital holography provides an overview of a vast array of research
activities in the field of digital holography. The paper consists of a series of 25 sections from …
activities in the field of digital holography. The paper consists of a series of 25 sections from …
On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry
Quantitative phase imaging has gained popularity in bioimaging because it can avoid the
need for cell staining, which, in some cases, is difficult or impossible. However, as a result …
need for cell staining, which, in some cases, is difficult or impossible. However, as a result …
Image-to-image regression with distribution-free uncertainty quantification and applications in imaging
Image-to-image regression is an important learning task, used frequently in biological
imaging. Current algorithms, however, do not generally offer statistical guarantees that …
imaging. Current algorithms, however, do not generally offer statistical guarantees that …
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
Deep learning-based image reconstruction methods have achieved remarkable success in
phase recovery and holographic imaging. However, the generalization of their image …
phase recovery and holographic imaging. However, the generalization of their image …
PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
Using a deep neural network, we demonstrate a digital staining technique, which we term
PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections …
PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections …
[HTML][HTML] Deep holography
G Situ - Light: Advanced Manufacturing, 2022 - light-am.com
With the explosive growth of mathematical optimization and computing hardware, deep
neural networks (DNN) have become tremendously powerful tools to solve many …
neural networks (DNN) have become tremendously powerful tools to solve many …
Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning
Simultaneous imaging of various facets of intact biological systems across multiple
spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is …
spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is …