SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology

1. 1Build a dual-head segmentation model using UNI2-H pathology foundation model and UperNet
2. 2Train the model on a large dataset of H&E-stained histology images
3. 3Configure the model to extract features and generate interpretable clinical reports
4. 4Test the model on a validation set to evaluate its performance
5. 5Apply the framework to characterize tumour microenvironment in various types of cancer

Pathologists, oncologists, and biomedical engineers can benefit from this framework to improve tumour microenvironment characterization, while data scientists and AI engineers can leverage it to develop more accurate cell segmentation models

💡 Foundation models like UNI2-H can be paired with UperNet to improve cell segmentation accuracy and enable interpretable clinical reporting

🔬 Introducing SegTME-UNI2: a unified framework for cell segmentation & LLM-driven tumour microenvironment characterization in histopathology! 🚀