Quantitative Phase Imaging and Artificial Intelligence: A Review

2018/06/06 by YoungJu Jo, Jo, YoungJu, Hyungjoo Cho +11 · 1 citation
Computer Science · Physics and Astronomy · #Advanced X-ray Imaging Techniques #Computer Vision and Pattern Recognition (cs.CV) #Data Analysis #Digital Holography and Microscopy #FOS: Computer and information sciences #FOS: Physical sciences #Optical measurement and interference techniques #Optics (physics.optics) #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.1806.03982

openalex publication_date 2018/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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