Upload SynthStroke synth_pseudo model

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+- medical
+- segmentation
+- stroke
+- neurology
+- mri
+pipeline_tag: image-segmentation
+---
+
+# SynthPseudo
+
+Synthseg-style model trained on synthetic data derived from OASIS3 tissue maps and ATLAS binary lesion masks. Augmented with pseudo-labels from a private T1w dataset.
+
+## Model Details
+
+- **Name**: SynthPseudo
+- **Classes**: 0 (Background), 1 (Gray Matter), 2 (White Matter), 3 (Gray/White Matter Partial Volume), 4 (Cerebro-Spinal Fluid), 5 (Stroke)
+- **Patch Size**: 192³
+- **Voxel Spacing**: 1mm³
+- **Input Channels**: 1
+
+## Usage
+
+### Loading from Hugging Face Hub
+
+```python
+import torch
+from synthstroke_model import SynthStrokeModel
+
+# Load the model from Hugging Face Hub
+model = SynthStrokeModel.from_pretrained("liamchalcroft/synthstroke-synth-pseudo")
+
+# 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 tissue probability maps
+background = predictions[:, 0]  # Background
+gray_matter = predictions[:, 1]  # Gray Matter
+white_matter = predictions[:, 2]  # White Matter
+partial_volume = predictions[:, 3]  # Gray/White Matter PV
+csf = predictions[:, 4]  # Cerebro-Spinal Fluid
+stroke = predictions[:, 5]  # Stroke lesion
+
+# Alternative: Get logits without TTA
+logits = model.predict_segmentation(input_tensor, apply_softmax=False)
+```
+
+## Citation
+
+```bibtex
+@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:
+
+```bibtex
+@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](https://github.com/liamchalcroft/synthstroke/blob/main/LICENSE) file for details.

model.safetensors

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