An Unsupervised Iterative Model for Single-Particle Cryo-EM Image Denoising Based on Siamese Neural Network

Abstract

Cryo-electron microscopy (cryo-EM) has become an important technology in the field of structural biology. Through the continuous development and improvement of hardware and software, more and more molecular biological structures close to atomic resolution have been resolved. In order to obtain accurate and reliable three-dimensional structure, clustering analysis of cryo-EM images is a very important and critical step.Different from traditional images, cryo-EM images have very high noise, in which the particles have random horizontal position and rotation directions. Most of the existing cryo-EM image processing tools implement traditional clustering methods, which do not perform well in such a highly-noisy scenario. In this paper, combined with the traditional K-means clustering algorithm, a new iterative clustering algorithm based on the unsupervised generative model is proposed. The iterative algorithm is mainly based on the idea of siamese network. First, we use K-means algorithm and Resnet to extract pre-labels for unlabeled data, and then in each subsequent iteration, we use siamese network to continuously extract and update the feature matrix of each image. After each epoch, K-means is adopted to cluster the image data based on the new feature representations. The contrastive loss is used as the loss function. The experimental results show that our method significantly improves the signal-to-noise ratio of images, and has better clustering performance compared with traditional methods.