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
Most evidence on the effectiveness of explainable artificial intelligence (XAI) attribution methods has been established on convolutional neural networks (CNNs), with limited investigation into whether these conclusions generalize to the diverse Vision Transformer (ViT) architectures that now dominate computer vision....
Sathiyamohan Nishankar, N. 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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