DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM

• URL: http://arxiv.org/abs/2410.11373v2
• Date: Mon, 28 Oct 2024 14:08:44 GMT
• Title: DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM
• Authors: Yingjun Shen, Haizhao Dai, Qihe Chen, Yan Zeng, Jiakai Zhang, Yuan Pei, Jingyi Yu,
• Abstract summary: DRACO is a Denoising-Reconstruction Autoencoder for cryogenic electron microscopy (cryo-EM) images. We build a high-quality, diverse dataset from an uncurated public database, including over 270,000 movies or micrographs. DRACO demonstrates the best performance in denoising, micrograph curation, and particle picking tasks compared to state-of-the-art baselines.
• Score: 27.092844681711195
• License:
• 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.

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