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Evaluation and Validation Framework for AI Models#

To ensure the reliability and accuracy of AI models trained on Med-ImageNet, a rigorous evaluation and validation framework is implemented:

  • Benchmark Datasets: Provides standardized benchmark datasets within Med-ImageNet to evaluate model performance across common metrics.
  • Cross-validation Protocols: Supports cross-validation to verify model robustness and prevent overfitting, ensuring that models generalize well across different patient groups.
  • Performance Metrics: Includes metrics for evaluating segmentation accuracy, predictive power, and clinical relevance of AI models.
  • Human-in-the-Loop Validation: Allows experts to review and validate AI model outputs, ensuring that predictions are clinically meaningful.
  • Continuous Model Improvement: Framework supports ongoing model evaluation, with feedback loops to integrate new data and improve performance over time.