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--- |
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license: unknown |
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--- |
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# iSight: AI-assisted Automatic Immunohistochemistry Staining Assessment |
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A deep learning-based multi-task prediction system for automated analysis of immunohistochemistry (IHC) pathology images and protein staining patterns. |
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## 🔗 Links |
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- **Code Repository**: [https://github.com/zhihuanglab/iSight](https://github.com/zhihuanglab/iSight) |
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- **Training Dataset**: [nirschl-lab/hpa10m](https://huggingface.co/datasets/nirschl-lab/hpa10m) |
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## 🎯 Prediction Tasks |
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The model simultaneously predicts 5 key attributes of IHC images: |
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| Task | Classes | Labels | |
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|------|---------|--------| |
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| **Staining Intensity** | 4 | negative, weak, moderate, strong | |
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| **Staining Location** | 4 | "none", "cytoplasmic/membranous", "nuclear", "cytoplasmic/membranous,nuclear" | |
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| **Staining Quantity** | 4 | none, <25%, 25%-75%, >75% | |
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| **Tissue Type** | 58 | Various human tissue types | |
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| **Malignancy** | 2 | normal, cancer | |
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## 📦 Model Files |
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``` |
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checkpoints/ |
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└── iSight_model_checkpoint.pth # Model weights |
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``` |
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## 🚀 Usage |
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### Run inference |
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Please refer to the [GitHub repository](https://github.com/zhihuanglab/iSight) for detailed inference instructions: |
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```bash |
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# Clone the repository |
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git clone https://github.com/zhihuanglab/iSight.git |
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cd iSight |
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# Run inference |
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bash inference_script.sh |
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``` |
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## 📧 Contact |
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For questions or suggestions, please contact: [zhi.huang@pennmedicine.upenn.edu](mailto:zhi.huang@pennmedicine.upenn.edu) or [jjnirschl@wisc.edu](mailto:jjnirschl@wisc.edu) |
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