VitaminP (Model Weights)

This repository hosts the pretrained model weights for VitaminP, a vision transformer–assisted multimodal framework for pathology cell and nuclei segmentation.

👉 Full codebase & documentation:
https://github.com/idso-fa1-pathology/VitaminP

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🔬 Model Description

VitaminP enables whole-cell and nuclei segmentation from H&E slides, without requiring immunofluorescence (MIF) at inference time.

The model is trained using paired H&E–MIF data and supports:

• H&E-only inference (standard pathology workflows)
• MIF-only inference
• Multimodal H&E + MIF inference (highest accuracy)

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⚠️ Important Notice (Research Use Only)

✅ Intended for:

• Research and development
• Computational pathology studies
• Algorithm benchmarking
• Educational purposes

❌ NOT intended for:

• Clinical diagnosis
• Patient care
• Medical decision-making

This model is for research use only and is not approved for clinical use.

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🧠 Available Weights

Model	Input	Description
flex	H&E / MIF / IHC	General-purpose model (recommended)
dual	H&E + MIF	Multimodal high-accuracy model
syn	H&E only	H&E-only whole-cell segmentation

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🚀 Usage

Load the model using the Python package:

import vitaminp

model = vitaminp.load_model("flex", device="cuda")

For full inference pipelines (WSI, Docker, CLI), see the GitHub repo: https://github.com/idso-fa1-pathology/VitaminP

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📦 Installation

pip install vitaminp

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📊 Outputs

• Cell and nuclei segmentation masks
• GeoJSON annotations (QuPath-compatible)
• Visualization overlays

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📚 Training Data

The model was trained on multiple public datasets:

• 14 datasets
• 34 cancer types
• 7M+ annotated cells

Includes paired H&E and multiplex immunofluorescence (MIF) data.

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⚖️ Limitations

• Performance varies across staining protocols and scanners
• Requires correct image resolution (MPP)
• Not validated for clinical deployment
• GPU recommended for large-scale inference

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🧭 Ethical Considerations

• May reflect dataset biases
• Not suitable for clinical use
• Should be validated before research use in new domains

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📄 Citation

@article{shokrollahi2025vitaminp,
  title   = {Vitamin-P: vision transformer assisted multi-modality integration network for pathology cell segmentation},
  author  = {Shokrollahi, Yasin and others},
  year    = {2025}
}

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🔗 Links

• GitHub (code & full docs):
  https://github.com/idso-fa1-pathology/VitaminP

• VitaminPScope viewer:
  https://github.com/idso-fa1-pathology/VitaminPScope

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📜 License

MIT License