Quantitative Phase Imaging and Artificial Intelligence: A Review

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 68
  • Download : 0
Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have opened up the possibility of an exciting frontier. The fast and label-free nature of QPI enables the rapid generation of large-scale and uniform-quality imaging data in two, three, and four dimensions. Subsequently, the AI-assisted interrogation ofQPI data using data-driven machine learning techniques results in a variety of biomedical applications. Also, machine learning enhances QPI itself. Herein, we review the synergy between QPI and machine learning with a particular focus on deep learning. Furthermore, we provide practical guidelines and perspectives for further development.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-01
Language
English
Article Type
Review
Keywords

DIGITAL HOLOGRAPHIC MICROSCOPY; OPTICAL DIFFRACTION TOMOGRAPHY; REFRACTIVE-INDEX TOMOGRAPHY; CONVOLUTIONAL NEURAL-NETWORKS; CELL IDENTIFICATION; AUTOMATIC IDENTIFICATION; LESION SEGMENTATION; INVERSE PROBLEMS; TIME; ALGORITHMS

Citation

IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, v.25, no.1

ISSN
1077-260X
DOI
10.1109/JSTQE.2018.2859234
URI
http://hdl.handle.net/10203/245859
Appears in Collection
PH-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0