An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis

1. 1Develop a vision-language model that incorporates both image and text data for LSS diagnosis
2. 2Implement adaptive PID-Tversky loss to address class imbalance and preserve spatial accuracy
3. 3Evaluate the model's performance on clinical segmentation datasets
4. 4Refine the model to improve explainability and reduce inter-observer variability

This research benefits data scientists and AI engineers working on medical imaging analysis, as it provides a novel approach to improving diagnosis accuracy and explainability

💡 Adaptive PID-Tversky loss can improve diagnosis accuracy and address class imbalance in clinical segmentation datasets

📸💡 Explainable vision-language model for lumbar spinal stenosis diagnosis