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nirschl-lab/
iSight

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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:

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 or jjnirschl@wisc.edu

nirschl-lab/iSight