From Machine Learning to Deep Learning: Revolutionizing Microscopy Image Analysis

Abstract Artificial intelligence (AI) has transformed microscopy workflows, enhancing efficiency from image acquisition to analysis. This article explores the evolution from conventional machine learning (ML) to deep learning (DL) in microscopy applications, discussing how AI assists at various stages of the microscopy process. It explains the fundamental differences between ML and DL, using real-world examples to demonstrate DL's superiority in complex scenarios such as organelle segmentation in life sciences and grain boundary analysis in materials sciences. The article also covers advanced topics like semantic and instance segmentation, providing insights into customizing DL models. By demystifying AI for microscopists, this work bridges the gap between cutting-edge technology and practical applications in microscopy.