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README.md

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-license: unknown
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+license: unknown
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+# 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)
+
+
+