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