Unlock the Power of Volumetric Microscopy with Deep Self-Learning
We are on the brink of a revolution in microscopy. Researchers from the University of Cambridge have developed a deep self-learning technique that enables volumetric microscopy with unprecedented precision and resolution. This groundbreaking technique could revolutionize the way that scientists, doctors, and engineers observe and analyze their experiments and samples.
The new technique, called Deep Self-Learning Volumetric Microscopy (DSLM), combines the power of machine learning with advances in optical hardware. By training a deep neural network to recognize and identify specific features within a sample, DSLM can automatically generate 3D images of cells and other microscopic objects with unprecedented precision and detail. This allows for unprecedented levels of detail in the observation and analysis of samples, enabling scientists to better understand the underlying biology and physics of their experiments.
source: Phys.org