ADD cryoGEM.

ZERONE182 · Nov 5, 2024 · 4cb064e · 4cb064e
1 parent 1e534f0
commit 4cb064e
Showing 2 changed files with 13 additions and 0 deletions.
diff --git a/_bibliography/papers.bib b/_bibliography/papers.bib
@@ -13,4 +13,17 @@ @article{Liu2023CryoFormerCR
   video={https://youtu.be/fSxdsiP_2DM?si=qzlnxpP8acwrW-Mk},
   preview={10180-oneturn.gif},
   selected={true},
+}
+
+@article{zhang2024cryogem,
+  title={CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy},
+  author={Zhang, Jiakai* and Chen, Qihe* and Zeng, Yan and Gao, Wenyuan and He, Xuming and Liu, Zhijie and Yu, Jingyi},
+  journal={Advances in Neural Information Processing Systems},
+  volume={38},
+  year={2024},
+  abstract={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 used as training data for particle picking and pose estimation models, eventually improving the reconstruction resolution.},
+  html={https://jiakai-zhang.github.io/cryogem/},
+  pdf={https://arxiv.org/pdf/2312.02235},
+  preview={cryogem-teaser.png},
+  selected={true},
 }
diff --git a/assets/img/publication_preview/cryogem-teaser.png b/assets/img/publication_preview/cryogem-teaser.png