2023Weiss Noise: Difference between revisions
Created page with "== Citation == G. Weiss-Dicker, A. Eldar, Y. Shkolinsky, and T. Bendory, “Unsupervised particle sorting for cryo-EM using probabilistic PCA,” in 2023 IEEE 20th Intl. Symposium on Biomedical Imaging (ISBI), IEEE, 2023, pp. 1–5. == Abstract == Single-particle cryo-electron microscopy (cryo-EM) is a leading technology to resolve the structure of molecules. Early in the process, the user detects potential particle images in the raw data. Typically, there are many fa..." |
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Latest revision as of 19:50, 23 December 2024
Citation
G. Weiss-Dicker, A. Eldar, Y. Shkolinsky, and T. Bendory, “Unsupervised particle sorting for cryo-EM using probabilistic PCA,” in 2023 IEEE 20th Intl. Symposium on Biomedical Imaging (ISBI), IEEE, 2023, pp. 1–5.
Abstract
Single-particle cryo-electron microscopy (cryo-EM) is a leading technology to resolve the structure of molecules. Early in the process, the user detects potential particle images in the raw data. Typically, there are many false detections as a result of high levels of noise and contamination. Currently, removing the false detections requires human intervention to sort the hundred thousands of images. We propose a statistically-established unsupervised algorithm to remove non-particle images. We model the particle images as a union of low-dimensional subspaces, assuming non-particle images are arbitrarily scattered in the high-dimensional space. The algorithm is based on an extension of the probabilistic PCA framework to robustly learn a non-linear model of union of subspaces. This provides a flexible model for cryo-EM data, and allows to automatically remove images that correspond to pure noise and contamination. Numerical experiments corroborate the effectiveness of the sorting algorithm.
Keywords
Links
https://ieeexplore.ieee.org/abstract/document/10230736