liamchalcroft/synthstroke-baseline

Baseline

Baseline model trained on real ATLAS T1w images.

Model Details

• Name: Baseline
• Classes: 0 (Background), 1 (Stroke)
• Patch Size: 192³
• Voxel Spacing: 1mm³
• Input Channels: 1 (T1w MRI)

Usage

Loading from Hugging Face Hub

import torch
from synthstroke_model import SynthStrokeModel

# Load the model from Hugging Face Hub
model = SynthStrokeModel.from_pretrained("liamchalcroft/synthstroke-baseline")

# Prepare your input (example shape: batch_size=1, channels=1, H, W, D)
input_tensor = torch.randn(1, 1, 192, 192, 192)

# Get predictions (with optional TTA for improved accuracy)
predictions = model.predict_segmentation(input_tensor, use_tta=True)

# Get lesion probability map (channel 1)
lesion_probs = predictions[:, 1]  # Shape: (batch_size, H, W, D)

# Alternative: Get logits without TTA
logits = model.predict_segmentation(input_tensor, apply_softmax=False)

Citation

Machine Learning for Biomedical Imaging

@article{chalcroft2025synthetic,
  title={Synthetic Data for Robust Stroke Segmentation},
  author={Chalcroft, Liam and Pappas, Ioannis and Price, Cathy J. and Ashburner, John},
  journal={Machine Learning for Biomedical Imaging},
  volume={3},
  pages={317--346},
  year={2025},
  publisher={Machine Learning for Biomedical Imaging},
  doi={10.59275/j.melba.2025-f3g6},
  url={https://www.melba-journal.org/papers/2025:014.html}
}

For the original arXiv preprint:

arXiv

@article{Chalcroft_2025,
   title={Synthetic Data for Robust Stroke Segmentation},
   volume={3},
   ISSN={2766-905X},
   url={http://dx.doi.org/10.59275/j.melba.2025-f3g6},
   DOI={10.59275/j.melba.2025-f3g6},
   number={August 2025},
   journal={Machine Learning for Biomedical Imaging},
   publisher={Machine Learning for Biomedical Imaging},
   author={Chalcroft, Liam and Pappas, Ioannis and Price, Cathy J. and Ashburner, John},
   year={2025},
   month=aug, pages={317–346} 
}

License

MIT License - see the LICENSE file for details.