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LN_segmentation

Image Segmentation
Transformers
Safetensors
PyTorch
segmentation
multilabel
unet
medical-imaging
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LN_segmentation
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---
license: mit
tags:
  - image-segmentation
  - multilabel
  - unet
  - pytorch
  - medical-imaging
library_name: transformers
pipeline_tag: image-segmentation
---

# LN_segmentation

A unet model for multilabel image segmentation trained with sliding window approach.

## Model Description

- **Architecture:** unet
- **Input Channels:** 3
- **Output Classes:** 4
- **Base Filters:** 32
- **Window Size:** 256

### Model-Specific Parameters


## Training Configuration

| Parameter | Value |
|-----------|-------|
| Batch Size | 64 |
| Learning Rate | 0.0003 |
| Weight Decay | 0.01 |
| Epochs | 100 |
| Patience | 10 |
| Dataset | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 |

## Performance Metrics

| Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 |
|--------|------|--------|--------|--------|--------|
| Dice | 0.5196 | 0.1800 | 0.2978 | 0.7189 | 0.8819 |
| IoU | 0.4059 | 0.0989 | 0.1749 | 0.5612 | 0.7887 |
| F1 | 0.5196 | 0.1800 | 0.2978 | 0.7189 | 0.8819 |
| MCC | 0.5044 | 0.1730 | 0.2861 | 0.7032 | 0.8554 |
| ROC AUC | 0.8338 | 0.6482 | 0.7772 | 0.9252 | 0.9847 |
| PR AUC | 0.4846 | 0.0767 | 0.1807 | 0.7583 | 0.9227 |


## Usage

```python
import numpy as np
from model import MODEL_REGISTRY, SegmentationConfig

# Load model
config = SegmentationConfig.from_pretrained("aholk/LN_segmentation")
model = MODEL_REGISTRY["unet"].from_pretrained("aholk/LN_segmentation")
model.eval()

# Run inference on a full image with sliding window
image = np.random.rand(2048, 2048, 3).astype(np.float32)  # Your image here
probs = model.predict_full_image(
    image,
    dim=256,
    batch_size=16,
    device="cuda"  # or "cpu"
)
# probs shape: (num_classes, H, W) with values in [0, 1]

# Threshold to get binary masks
masks = (probs > 0.5).astype(np.uint8)
```

## Training Plots

![Training Loss](training_loss.png)
![Dice Curves](dice_curves.png)
![MCC Curves](mcc_curves.png)
![Best Validation](best_validation_reconstruction.png)

## Citation

If you use this model, please cite:

```bibtex
@software{windowz_segmentation,
  title={Multilabel Image Segmentation with Sliding Window U-Net},
  author={Gleghorn Lab},
  year={2025},
  url={https://github.com/GleghornLab/ComputerVision2}
}
```