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A systematic architecture-aware evaluation protocol is presented to assess the effectiveness of nine widely used XAI techniques across four categories of DL models: very deep, lightweight, transformer-based and hybrid neural networks.
Deep neural networks play a significant role in medical image analysis, particularly in improving the efficiency and accuracy of disease diagnosis and treatment planning. The ability to preserve the privacy of medical data opens the door to harnessing more information to train powerful and intelligent AI models. Howeve...
A controlled benchmark that evaluates attribution quality across five dimensions: faithfulness, localization, robustness, complexity, and computational cost shows that attribution performance is strongly architecture-dependent and that rankings established on CNNs do not reliably transfer to transformer-based models.
HiLRP alone preserves conservation across windowed, spatial-reduction, multi-axis, and linear-attention models, where naive extensions can produce zero or inflated relevance, and localizes attribution failures in class activation mapping.
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