LN_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

Performance Metrics

Usage

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:

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