Quantitative Phase Imaging and Artificial Intelligence: A Review
arXiv:1806.03982
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.
References in corpus (28)
- Adam: A Method for Stochastic Optimization
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Attention U-Net: Learning Where to Look for the Pancreas
- Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
- Striving for Simplicity: The All Convolutional Net
- Deep Convolutional Neural Network for Inverse Problems in Imaging
- Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
- Measuring the Transmission Matrix in Optics : An Approach to the Study and Control of Light Propagation in Disordered Media
- Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
- Temporal Ensembling for Semi-Supervised Learning
- Deep Learning for Identifying Metastatic Breast Cancer
- PathNet: Evolution Channels Gradient Descent in Super Neural Networks
- Efficient Neural Architecture Search via Parameter Sharing
- Shake-Shake regularization
- FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
- Recent advances in wavefront shaping techniques for biomedical applications
- Neural Optimizer Search with Reinforcement Learning
- High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
- Quantitative phase microscopy spatial signatures of cancer cells
- Neural Stain-Style Transfer Learning using GAN for Histopathological Images
- Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles
- Optical diffraction tomography using a digital micromirror device for stable measurements of 4-D refractive index tomography of cells
- Label-free identification of individual bacteria using Fourier transform light scattering
- DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
- Convolutional neural networks that teach microscopes how to image
- Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes
- Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow