Explainable Artificial Intelligence (XAI) has become essential in medical image analysis to ensure transparency of deep learning (DL)-based diagnostic systems. However, selecting appropriate XAI techniques for breast cancer recognition remains largely ad hoc, with limited systematic evaluation across different DL archi...
B. Thanusanth, Selvarajah Thuseethan, R. Ragel et al.· 0 citations
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...
Linkon Chowdhury, Selvarajah Thuseethan, Yakub Sebastian et al.· IEEE Transactions on Emergin...· 0 citations
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.
Sathiyamohan Nishankar, Nethmi Pathirana, Pubudu Sanjeewani et al.· 0 citations
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.
Sathiyamohan Nishankar, Pubudu Sanjeewani, Asanka Perera et al.· 0 citations
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