WikiSysop: Created page with "== Citation == P. Harar, L. Herrmann, P. Grohs, and D. Haselbach, “Faket: Simulating cryo-electron tomograms with neural style transfer,” Structure, vol. 33, no. 4, pp. 820–827, 2025. == Abstract == In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training datasets. The protracted generation time of physics-based models, often employed to produce th..."

2025-04-23T07:12:05Z

Created page with "== Citation == P. Harar, L. Herrmann, P. Grohs, and D. Haselbach, “Faket: Simulating cryo-electron tomograms with neural style transfer,” Structure, vol. 33, no. 4, pp. 820–827, 2025. == Abstract == In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training datasets. The protracted generation time of physics-based models, often employed to produce th..."

New page

== Citation ==

P. Harar, L. Herrmann, P. Grohs, and D. Haselbach, “Faket: Simulating cryo-electron tomograms with neural style transfer,” Structure, vol. 33, no. 4, pp. 820–827, 2025.

== Abstract ==

In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep
learning solutions, though successful, require extensive training datasets. The protracted generation time
of physics-based models, often employed to produce these datasets, limits their broad applicability. We
introduce FakET, a method based on neural style transfer, capable of simulating the forward operator of
any cryo transmission electron microscope. It can be used to adapt a synthetic training dataset according
to reference data producing high-quality simulated micrographs or tilt-series. To assess the quality of our
generated data, we used it to train a state-of-the-art localization and classification architecture and
compared its performance with a counterpart trained on benchmark data. Remarkably, our technique
matches the performance, boosts data generation speed 7503 , uses 333 less memory, and scales well
to typical transmission electron microscope detector sizes. It leverages GPU acceleration and parallel processing.
The source code is available at https://github.com/paloha/faket/.

== Keywords ==

== Links ==

https://www.cell.com/structure/fulltext/S0969-2126(25)00020-6

== Related software ==

== Related methods ==

== Comments ==

2025Harar FakET - Revision history