Electron Microscopy

Electron microscopy (EM) generates high-resolution images of materials at the nano- and atomic scale, crucial for characterizing diverse materials and biological structures. Current research emphasizes developing advanced computational methods, including deep learning architectures like convolutional neural networks (CNNs), generative adversarial networks (GANs), and hypergraph neural networks (HgNNs), to automate image analysis, improve segmentation accuracy (especially with limited labeled data), and enhance image resolution through super-resolution techniques. These advancements are significantly impacting materials science, biology, and medicine by enabling faster, more efficient, and higher-throughput analysis of complex datasets, leading to improved material design and a deeper understanding of biological systems.