# Quantitative Phase Imaging and Artificial Intelligence: A Review

arXiv: https://arxiv.org/abs/1806.03982 · HTML: https://mlsanity.com/paper/1806.03982/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 13 of 13)

- [Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging](https://mlsanity.com/paper/2202.05265/) (2022)
- [Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization](https://mlsanity.com/paper/2204.10533/) (2022)
- [Self-supervised learning of hologram reconstruction using physics consistency](https://mlsanity.com/paper/2209.08288/) (2022)
- [eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging](https://mlsanity.com/paper/2301.03162/) (2023)
- [Digital staining in optical microscopy using deep learning -- a review](https://mlsanity.com/paper/2303.08140/) (2023)
- [Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks](https://mlsanity.com/paper/2206.10152/) (2022)
- [Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain](https://mlsanity.com/paper/2103.09022/) (2021)
- [Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor](https://mlsanity.com/paper/2401.08923/) (2024)
- [Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor](https://mlsanity.com/paper/2403.11035/) (2024)
- [From Hours to Seconds: Towards 100x Faster Quantitative Phase Imaging via Differentiable Microscopy](https://mlsanity.com/paper/2205.11521/) (2022)
- [Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis](https://mlsanity.com/paper/2208.08834/) (2022)
- [Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning](https://mlsanity.com/paper/2312.07102/) (2023)
- [Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging](https://mlsanity.com/paper/2405.06175/) (2024)

## Abstract (arXiv)

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.