The field of artificial intelligence (AI) has advanced significantly in recent years. Recently, digital holographic imaging has become a potent new tool that may be used to discover new cellular biomarkers and investigate cell dynamics and structure with nanometric axial sensitivity. This method can reach a record-high accuracy in non-invasive, label-free cellular phenotypic screening by fusing digital holography with AI technologies, including latest deep learning algorithms. It creates a fresh avenue for data-driven diagnosis.

Using methods from generative adversarial networks, convolutional neural networks, and artificial neural networks, Artificial Intelligence in Digital Holographic Imaging presents fundamental ideas and algorithms of AI to demonstrate how to develop intelligent holographic imaging systems. The fundamentals of integrating AI into holographic imaging system designs and making the connections between real-world biomedical issues raised by the application of digital holography and different AI algorithms in intelligence models will be made clear to readers.

What’s Contained Background information on digital holography Fundamental ideas in digital holographic imaging Deep learning methods for holographic visualization AI methods for analyzing holographic images Holographic models for image categorization Automated live cell phenotypic analysis

This book offers a thorough analysis of the application of intelligent holographic imaging systems in biological domains, catering to a wide range of readers with diverse backgrounds.