Gradient Attention Map Based Verification of Deep Convolutional Neural Networks with Application to X-ray Image Datasets

Deep learning models have great potential in medical imaging, including orthodontics and skeletal maturity assessment. However, using a model on data different from its training set can lead to unreliable predictions that may impact patient care. To address this, we introduce a Gradient Attention Map (GAM)-based framework that evaluates a model’s suitability for new data by examining its attention patterns. Using Grad-CAM, we generate attention maps and compare them with metrics such as IoU, Dice Similarity, SSIM, Cosine Similarity, Pearson Correlation, KL Divergence, and Wasserstein Distance. A Random Forest classifier then distinguishes between models that are well-suited and those that are misapplied. Experimental results show that our method effectively filters out unsuitable models, promoting safer and more reliable use of deep learning in medical imaging.