@inproceedings{
zhang2024cryogem,
title={Cryo{GEM}: Physics-Informed Generative Cryo-Electron Microscopy},
author={Jiakai Zhang and Qihe Chen and Yan Zeng and Wenyuan Gao and Xuming He and Zhijie Liu and Jingyi Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=edOZifvwMi}
}
In the past decade, deep conditional generative models have revolutionized the generation of realistic images, extending their application from entertainment to scientific domains. Single-particle cryo-electron microscopy (cryo-EM) is crucial in resolving near-atomic resolution 3D structures of proteins, such as the SARS-COV-2 spike protein. To achieve high-resolution reconstruction, a comprehensive data processing pipeline has been adopted. However, its performance is still limited as it lacks high-quality annotated datasets for training. To address this, we introduce physics-informed generative cryo-electron microscopy (CryoGEM), which for the first time integrates physics-based cryo-EM simulation with a generative unpaired noise translation to generate physically correct synthetic cryo-EM datasets with realistic noises. Initially, CryoGEM simulates the cryo-EM imaging process based on a virtual specimen. To generate realistic noises, we leverage an unpaired noise translation via contrastive learning with a novel mask-guided sampling scheme. Extensive experiments show that CryoGEM is capable of generating authentic cryo-EM images. The generated dataset can be used as training data for particle picking and pose estimation models, eventually improving the reconstruction resolution.
CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy
Jiakai Zhang Qihe Chen Yan Zeng Wenyuan Gao Xuming He Zhijie Liu Jingyi Yu