WikiSysop: Created page with "== Citation == A. Zamanos, P. Koromilas, G. Bouritsas, P. L. Kastritis, and Y. Panagakis, “Self-supervised learning for generalizable particle picking in cryo-EM micrographs,” Cell Reports Methods, 2025. == Abstract == We present cryoelectron microscopy masked autoencoder (cryo-EMMAE), a self-supervised method designed to overcome the need for manually annotated cryo-EM data. cryo-EMMAE leverages the representation space of a masked autoencoder to pick particle pi..."

2025-07-15T06:42:27Z

Created page with "== Citation == A. Zamanos, P. Koromilas, G. Bouritsas, P. L. Kastritis, and Y. Panagakis, “Self-supervised learning for generalizable particle picking in cryo-EM micrographs,” Cell Reports Methods, 2025. == Abstract == We present cryoelectron microscopy masked autoencoder (cryo-EMMAE), a self-supervised method designed to overcome the need for manually annotated cryo-EM data. cryo-EMMAE leverages the representation space of a masked autoencoder to pick particle pi..."

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== Citation ==

A. Zamanos, P. Koromilas, G. Bouritsas, P. L. Kastritis, and Y. Panagakis, “Self-supervised learning for generalizable particle picking in cryo-EM micrographs,” Cell Reports Methods, 2025.

== Abstract ==

We present cryoelectron microscopy masked autoencoder (cryo-EMMAE), a self-supervised method designed
to overcome the need for manually annotated cryo-EM data. cryo-EMMAE leverages the representation
space of a masked autoencoder to pick particle pixels through clustering of the MAE latent representation.
Evaluation across different EMPIAR datasets demonstrates that cryo-EMMAE outperforms state-of-the-art
supervised methods in terms of generalization capabilities. Importantly, our method showcases consistent
performance, independent of the dataset used for training. Additionally, cryo-EMMAE is data efficient, as
we experimentally observe that it converges with as few as five micrographs. Further, 3D reconstruction results
indicate that our method has superior performance in reconstructing the volumes in both single-particle
datasets and multi-particle micrographs derived from cell extracts. Our results underscore the potential of
self-supervised learning in advancing cryo-EM image analysis, offering an alternative for more efficient and
cost-effective structural biology research. Code is available at https://github.com/azamanos/Cryo-EMMAE.

== Keywords ==

== Links ==

https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(25)00125-0

== Related software ==

== Related methods ==

== Comments ==

2025Zamanos CryoEMMAE - Revision history