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

• URL: http://arxiv.org/abs/2504.21227v1
• Date: Tue, 29 Apr 2025 23:41:37 GMT
• Title: Gradient Attention Map Based Verification of Deep Convolutional Neural Networks with Application to X-ray Image Datasets
• Authors: Omid Halimi Milani, Amanda Nikho, Lauren Mills, Marouane Tliba, Ahmet Enis Cetin, Mohammed H. Elnagar,
• Abstract summary: We propose a comprehensive verification framework that evaluates model suitability through multiple complementary strategies.<n>First, we introduce a Gradient Attention Map (GAM)-based approach that analyzes attention patterns using Grad-CAM.<n>Second, we extend verification to early convolutional feature maps, capturing structural mis-alignments missed by attention alone.<n>Third, we incorporate an additional garbage class into the classification model to explicitly reject out-of-distribution inputs.
• Score: 1.0208529247755187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models have great potential in medical imaging, including orthodontics and skeletal maturity assessment. However, applying a model to data different from its training set can lead to unreliable predictions that may impact patient care. To address this, we propose a comprehensive verification framework that evaluates model suitability through multiple complementary strategies. First, we introduce a Gradient Attention Map (GAM)-based approach that analyzes attention patterns using Grad-CAM and compares them via similarity metrics such as IoU, Dice Similarity, SSIM, Cosine Similarity, Pearson Correlation, KL Divergence, and Wasserstein Distance. Second, we extend verification to early convolutional feature maps, capturing structural mis-alignments missed by attention alone. Finally, we incorporate an additional garbage class into the classification model to explicitly reject out-of-distribution inputs. Experimental results demonstrate that these combined methods effectively identify unsuitable models and inputs, promoting safer and more reliable deployment of deep learning in medical imaging.

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