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# EMcopilot: Your label-free copilot for automated electron microscopy image analysis |
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## Training and Code Details |
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For detailed training procedures and source code, please refer to our GitHub repository: |
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[EMcopilot GitHub Repository](https://github.com/WenhaoYuan337/EMcopilot) |
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## Usage |
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1. **Train and predict segmentation models**: |
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* `00_01_sam_binary_masking.py` - Generates coarse masks using the SAM model. |
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3. **Generate and analyze synthetic masks**: |
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* `02_01_sam_mask_analysis.py` - Analyzes SAM mask properties and extracts morphology prior. |
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* `02_02_random_mask_generate.py` - Generates synthetic masks by augmenting existing masks. |
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5. **Generate and evaluate images**: |
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* `03_01_p2p_train.py` - Trains a Pix2Pix model for mask-to-EMimage translation. |
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* `04_01_pix2pix_predict.py` - Runs inference using the trained Pix2Pix model. |
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6. **Domain Adaptation**: |
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* `05_01_domain_adaptation.py` - Applies domain adaptation, including noise and contrast augmentation. |
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7. **UNet++ Training and Inference**: |
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* `06_01_unet++_train.py` - Trains a CBAM-enhanced UNet++ model for segmentation. |
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* `07_01_unet++_predict.py` - Performs inference using the trained UNet++ model. |
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8. **Analyze DM4 microscopy data**: |
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* `08_01_in_situ_analysis.py` - Analyze HAADF-STEM images of supported nanoparticles in real time. |
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## Installation |
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Install required packages using: |
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```bash |
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pip install -r requirements.txt |
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``` |
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## Citation |
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If you find our code or data useful in your research, please cite our paper: |
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``` |
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@misc{yuan2024deepgenerativemodelsassistedautomated, |
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title={Deep Generative Models-Assisted Automated Labeling for Electron Microscopy Images Segmentation}, |
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author={Wenhao Yuan and Bingqing Yao and Shengdong Tan and Fengqi You and Qian He}, |
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year={2024}, |
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eprint={2407.19544}, |
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archivePrefix={arXiv}, |
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primaryClass={cond-mat.mtrl-sci}, |
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url={https://arxiv.org/abs/2407.19544}, |
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} |
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``` |
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Copyright | 2025 Qian's Lab@NUS |
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