WikiSysop: Created page with "== Citation == Zhang, Dongxu / Yan, Yang / Huang, Yulin / Liu, Bowen / Zheng, Qingbing / Zhang, Jun / Xia, Ningshao. Unsupervised Cryo-EM Images Denoising and Clustering base..."

2023-07-25T07:59:49Z

Created page with "== Citation == Zhang, Dongxu / Yan, Yang / Huang, Yulin / Liu, Bowen / Zheng, Qingbing / Zhang, Jun / Xia, Ningshao. Unsupervised Cryo-EM Images Denoising and Clustering base..."

New page

== Citation ==

Zhang, Dongxu / Yan, Yang / Huang, Yulin / Liu, Bowen / Zheng, Qingbing / Zhang, Jun / Xia, Ningshao. Unsupervised Cryo-EM Images Denoising and Clustering based on Deep Convolutional Autoencoder and K-Means++. 2023. IEEE Trans. Medical Imaging, Vol. 42, p. 1509-1521

== Abstract ==

Cryo-electron microscopy (cryo-EM) is a
widely used structural determination technique. Because
of the extremely low signal-to-noise ratio (SNR) of images
captured by cryo-EM, clustering single-particle cryo-EM
images with high accuracy is challenging. To address this,
we proposed an iterative denoising and clustering method
based on a deep convolutional variational autoencoder and
K-means+ +. The proposed method contains two modules:
a denoising ResNet variational autoencoder (DRVAE), and
Balance size K-means++ (BSK-means++). First, the DRVAE
is trained in a fully unsupervised manner to initialize the
neural network and obtain preliminary denoised images.
Second, BSK-means++ is built for clustering denoised
images, and images closer to class centers are divided into
reliable samples. Third, the training of DRVAE is continued,
while the class-average images are used as pseudo supervision of reliable samples to reserve more detailed information of denoised images. Finally, the second and third steps
mentioned above can be performed jointly and iteratively
until convergence occurs. The experimental results showed
that the proposed method can generate reliable class average images and achieve better clustering accuracy and
normalized mutual information than current methods. This
study confirmed that DRVAE with BSK-means++ could
achieve a good denoise performance on single-particle
cryo-EM images, which can help researchers obtain information such as symmetry and heterogeneity of the target
particles. In addition, the proposed method avoids the
extreme imbalance of class size, which improves the reliability of the clustering result.

== Keywords ==

== Links ==

https://ieeexplore.ieee.org/abstract/document/9997544

== Related software ==

== Related methods ==

== Comments ==

2022Zhang DRVAE - Revision history

WikiSysop: Created page with "== Citation == Zhang, Dongxu / Yan, Yang / Huang, Yulin / Liu, Bowen / Zheng, Qingbing / Zhang, Jun / Xia, Ningshao. Unsupervised Cryo-EM Images Denoising and Clustering base..."