Instructions to use GleghornLab/lymph_node_segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GleghornLab/lymph_node_segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="GleghornLab/lymph_node_segmentation")# Load model directly from transformers import UNetForSegmentation model = UNetForSegmentation.from_pretrained("GleghornLab/lymph_node_segmentation", dtype="auto") - Notebooks
- Google Colab
- Kaggle
updated to clarify the metrics are from the test scores not the validation scores
Browse files
README.md
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| Patience | 10 |
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| Dataset | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 |
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## Performance Metrics
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| Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 |
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| Patience | 10 |
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| Dataset | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 |
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## Test Performance Metrics
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| Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 |
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