Add DRACO bib.

ZERONE182 · Nov 8, 2024 · 7e07b18 · 7e07b18
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commit 7e07b18
Showing 2 changed files with 13 additions and 1 deletion.
diff --git a/_bibliography/papers.bib b/_bibliography/papers.bib
@@ -20,12 +20,24 @@ @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.},
   website={https://jiakai-zhang.github.io/cryogem/},
   code={https://github.com/Cellverse/CryoGEM/tree/main},
   pdf={https://arxiv.org/pdf/2312.02235},
   preview={cryogem-teaser.png},
   selected={true},
+}
+
+@article{shen2024draco,
+  bibtex_show={true},
+  title={Draco: Denoising Reconstruction Autoencoder for CryO-EM},
+  author={Shen*, Yingjun and Dai*, Haizhao and Chen, Qihe and Zeng, Yan and Zhang, Jiakai and Pei, Yuan and Yu, Jingyi},
+  journal={Advances in Neural Information Processing Systems},
+  year={2024},
+  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. We will release the code, pre-trained models, and the curated dataset to stimulate further research.},
+  website={https://duskngai.github.io/draco/},
+  pdf={https://duskngai.github.io/draco/static/DRACO.pdf},
+  preview={draco-teaser.png},
+  selected={true},
 }
diff --git a/assets/img/publication_preview/draco-teaser.png b/assets/img/publication_preview/draco-teaser.png