metadata
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 |
Performance Metrics
| Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 |
|---|---|---|---|---|---|
| Dice | 0.7781 | 0.7197 | 0.7614 | 0.7271 | 0.9040 |
| IoU | 0.6524 | 0.5708 | 0.6199 | 0.5929 | 0.8261 |
| F1 | 0.7781 | 0.7197 | 0.7614 | 0.7271 | 0.9040 |
| MCC | 0.7832 | 0.7308 | 0.7730 | 0.7440 | 0.8850 |
| ROC AUC | 0.9945 | 0.9945 | 0.9975 | 0.9917 | 0.9943 |
| PR AUC | 0.9076 | 0.8357 | 0.9108 | 0.9095 | 0.9743 |
Usage
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:
@software{windowz_segmentation,
title={Multilabel Image Segmentation with Sliding Window U-Net},
author={Gleghorn Lab},
year={2025},
url={https://github.com/GleghornLab/ComputerVision2}
}