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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
- Training Dataset: 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 for detailed inference instructions:
# 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 or jjnirschl@wisc.edu