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CryoCCD: A Unified Framework for Cryo-EM Synthetic Data Generation
Outline
CryoCCD is a synthetic data generation framework developed to address the lack of high-quality annotated data for single-molecule cryo-electron microscopy (cryo-EM) analysis. This framework effectively reproduces the complex noise and biological heterogeneity of real-world images by integrating various biophysical modeling and a cryo-EM-specific conditional cycle-consistent diffusion model. CryoCCD generates structurally faithful micrographs, improves particle selection and pose estimation, and outperforms existing methods.
Takeaways, Limitations
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Takeaways:
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Various biophysical modeling for accurate description of biological tissues.
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A diffusion model using cycle consistency and mask-guided contrastive learning to maintain structural fidelity.
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Improved particle selection and pose estimation performance.