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

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}
}