DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM

TL;DR

DRACO is a novel autoencoder model designed specifically for cryo-EM images, effectively denoising and reconstructing images by leveraging a large, high-quality dataset and self-supervised training, improving downstream cryo-EM tasks.

Contribution

Introduces DRACO, a denoising-reconstruction autoencoder tailored for cryo-EM, utilizing a hybrid training scheme and a large dataset to enhance image quality and downstream analysis.

Findings

DRACO outperforms state-of-the-art methods in denoising cryo-EM images.

Pre-training on a large dataset improves generalization to various cryo-EM tasks.

DRACO serves as a versatile foundation model for cryo-EM image analysis.

Abstract

Foundation models in computer vision have demonstrated exceptional performance in zero-shot and few-shot tasks by extracting multi-purpose features from large-scale datasets through self-supervised pre-training methods. However, these models often overlook the severe corruption in cryogenic electron microscopy (cryo-EM) images by high-level noises. We introduce DRACO, a Denoising-Reconstruction Autoencoder for CryO-EM, inspired by the Noise2Noise (N2N) approach. By processing cryo-EM movies into odd and even images and treating them as independent noisy observations, we apply a denoising-reconstruction hybrid training scheme. We mask both images to create denoising and reconstruction tasks. For DRACO's pre-training, the quality of the dataset is essential, we hence build a high-quality, diverse dataset from an uncurated public database, including over 270,000 movies or micrographs. After pre-training, DRACO naturally serves as a generalizable cryo-EM image denoiser and a foundation model for various cryo-EM downstream tasks. DRACO demonstrates the best performance in denoising, micrograph curation, and particle picking tasks compared to state-of-the-art baselines.