---
license: mit
tags:
  - image-segmentation
  - multilabel
  - unet
  - pytorch
  - medical-imaging
library_name: transformers
pipeline_tag: image-segmentation
---

# LN_segmentation_sweep

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

## Model Description

- **Architecture:** unet
- **Input Channels:** 3
- **Output Classes:** 4
- **Base Filters:** 128
- **Window Size:** 128
- **Downsample Factor:** 1.0

### Model-Specific Parameters


## Training Configuration

| Parameter | Value |
|-----------|-------|
| Batch Size | 8 |
| Learning Rate | 7.77451918775676e-06 |
| Weight Decay | 0.00164040349077736 |
| Epochs | 100 |
| Patience | 10 |
| Dataset | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 |


## Validation Performance Metrics (Pooled Pixel-Level)

| Metric       |   Mean | Class 0 | Class 1 | Class 2 | Class 3 |
| ------------ | -----: | ------: | ------: | ------: | ------: |
| Dice         | 0.8697 |  0.7635 |  0.8607 |  0.9085 |  0.9461 |
| IoU          | 0.7757 |  0.6174 |  0.7554 |  0.8323 |  0.8977 |
| F1           | 0.8697 |  0.7635 |  0.8607 |  0.9085 |  0.9461 |
| MCC          | 0.8649 |  0.7636 |  0.8588 |  0.9032 |  0.9340 |
| ROC AUC      | 0.9971 |  0.9946 |  0.9983 |  0.9978 |  0.9978 |
| PR AUC       | 0.9412 |  0.8527 |  0.9494 |  0.9720 |  0.9908 |


## Test Performance Metrics (Pooled Pixel-Level)

| Metric       |   Mean | Class 0 | Class 1 | Class 2 | Class 3 |
| ------------ | -----: | ------: | ------: | ------: | ------: |
| Dice         | 0.7960 |  0.7578 |  0.7830 |  0.7344 |  0.9087 |
| IoU          | 0.6666 |  0.6101 |  0.6433 |  0.5803 |  0.8328 |
| F1           | 0.7960 |  0.7578 |  0.7830 |  0.7344 |  0.9087 |
| MCC          | 0.7959 |  0.7609 |  0.7891 |  0.7436 |  0.8900 |
| ROC AUC      | 0.9942 |  0.9949 |  0.9975 |  0.9903 |  0.9941 |
| PR AUC       | 0.9117 |  0.8628 |  0.9133 |  0.8985 |  0.9720 |


## Usage

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

# Load model
config = SegmentationConfig.from_pretrained("aholk/LN_segmentation_sweep")
model = MODEL_REGISTRY["unet"].from_pretrained("aholk/LN_segmentation_sweep")
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=128,
    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)
```


## 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}
}
```