Attention-based multiple instance learning for predominant growth pattern prediction in lung adenocarcinoma wsi using foundation models

1. 1Apply attention-based multiple instance learning to whole slide images of lung adenocarcinoma
2. 2Use foundation models as a starting point for the ABMIL framework
3. 3Configure the model to focus on predominant growth patterns
4. 4Train the model on a dataset of annotated whole slide images
5. 5Evaluate the model's performance using metrics such as accuracy and F1-score

This research benefits pathologists, oncologists, and AI engineers working on medical image analysis, as it provides a novel approach to predicting growth patterns in lung adenocarcinoma

💡 Attention-based multiple instance learning can effectively predict predominant growth patterns in lung adenocarcinoma whole slide images, reducing the need for extensive annotations

🚀 Attention-based multiple instance learning for lung adenocarcinoma growth pattern prediction! 🎯 Improving prognosis and treatment decisions with AI 🚀