Image Processing - Health

How can Thermo Fisher use machine learning to identify the best Electron Microscopy samples?

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Case Thermo Fisher Scientific

Analyzing biological samples in a Transmission Electron Microscope to the utmost achievable performance, often represents a real challenge.
Biological samples, placed on so-called mesh grids, often need to be in their natural state. This requires a specific sample preparation process called vitrification. This can lead to all kind of errors on the sample, so identifying the good location on the sample is crucial. Could you think of a trusted method for creating a classifier that selects good vitrified sample locations in a mesh grid? Which parameters will you take into account? Could you link these parameters to physical properties or phenomena related to vitrification?

Recommended background and tools

Core knowledge:
Image processing / machine learning

Physics / optics / material science

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Thermo Fisher Scientific Inc. (NYSE: TMO) is the world leader in serving science. Our mission is to enable our customers to make the world healthier, cleaner and safer. We help our customers accelerate life sciences research, solve complex analytical challenges, improve patient diagnostics and increase laboratory productivity.

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