Scientists developing deep learning AI tools are expected to reshape microscopy technology

2024-03-07

The research team at the University of Gothenburg has recently developed an AI tool that can help analyze images captured by monitors. This AI tool has been widely recognized and is expected to bring qualitative changes to existing microscopes, paving the way for research in both scientific and industrial fields.

Benjamin Midtvedt, the main author of the study and a PhD student in physics, said, "Deep learning has become popular worldwide and has had a huge impact on many industries, sectors, and scientific fields. We have now developed a tool to use deep learning to analyze and explore images captured under microscopes.".

Deep learning can be described as a mathematical model used to solve problems that are difficult to solve using traditional algorithmic methods. In microscopes, the huge challenge is how to retrieve as much information as possible from data packaged images, which is also where deep learning has been proven to be very effective.

The tool developed by Midtvedt and his research colleagues involves neural network learning, which accurately retrieves the information researchers want from a large amount of images, known as training data. Compared to having to manually create training data, this tool simplifies the process of creating training data, allowing for the generation of tens of thousands of images within an hour, rather than generating 100 images within a month.

Midtvedt stated, "This makes it possible to quickly extract more details from microscope images without the need to create a complex analysis using traditional methods. In addition, the results are repeatable and can retrieve customized specific information for specific purposes.".

For example, this tool allows users to determine the size and material characteristics of very small particles, and easily count and classify cells. Researchers have demonstrated that this tool can be used by industries that need to purify emissions, as they can see in real-time whether all unwanted particles have been filtered out. Researchers hope that in the future, this tool can be used to track infections in cells and draw maps of cell defense mechanisms, which will bring great possibilities for new drugs and treatment methods.

He said, "We have seen significant interest in this tool from the international community. Regardless of the micro challenges faced, researchers can now more easily analyze, make new discoveries, implement ideas, and open up new areas in their field.".

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