Quantitative Phase Imaging and Artificial Intelligence: A Review
Computer Vision and Pattern Recognition
2018-07-16 v2 Data Analysis, Statistics and Probability
Optics
Abstract
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 of QPI 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. Further, we provide practical guidelines and perspectives for further development.
Cite
@article{arxiv.1806.03982,
title = {Quantitative Phase Imaging and Artificial Intelligence: A Review},
author = {YoungJu Jo and Hyungjoo Cho and Sang Yun Lee and Gunho Choi and Geon Kim and Hyun-seok Min and YongKeun Park},
journal= {arXiv preprint arXiv:1806.03982},
year = {2018}
}