CryoGEN: Generative Energy-based Models for Cryogenic Electron Tomography Reconstruction
Yunfei Teng, Yuxuan Ren, Kai Chen, Xi Chen, Zhaoming Chen, Qiwei Ye
Abstract
Cryogenic electron tomography (Cryo-ET) is a powerful technique for visualizing subcellular structures in their native states. Nonetheless, its effectiveness is compromised by anisotropic resolution artifacts caused by the missing-wedge effect. To address this, IsoNet, a deep learning-based method, proposes iteratively reconstructing the missing-wedge information. While successful, IsoNet's dependence on recursive prediction updates often leads to training instability and model divergence. In this study, we introduce CryoGEN—an energy-based probabilistic model that not only mitigates resolution anisotropy but also removes the need for recursive subtomogram averaging, delivering an approximate *10*$\times$ speedup for training. Evaluations across various biological datasets, including immature HIV-1 virions and ribosomes, demonstrate that CryoGEN significantly enhances structural completeness and interpretability of the reconstructed samples.
BibTeX
@inproceedings{
teng2025cryogen,
title={Cryo{GEN}: Generative Energy-based Models for Cryogenic Electron Tomography Reconstruction},
author={Yunfei Teng and Yuxuan Ren and Kai Chen and Xi Chen and Zhaoming Chen and Qiwei Ye},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=uOb7rij7sR}
}