33 citations · 57 across the 10 of their papers we have counts for
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Architecture-aware Robustness Evaluation of Explainable Deep Learning for Breast Cancer Diagnosis
Balenthira Thanusanth, Selvarajah Thuseethan, Roshan G. Ragel +3
Explainable Artificial Intelligence (XAI) has become essential in medical image analysis to ensure transparency of deep learning (DL)-based diagnostic systems. However, selecting a…
HiLRP: Toward One Trustworthy Explanation for Vision Transformer: Conservation-Valid Attribution via Attention Primitives
Sathiyamohan Nishankar, Pubudu Sanjeewani, Asanka Perera +1
Vision Transformer (ViT) design has become increasingly diverse, with backbones combining convolutional stems, windowed, linear, or multi-axis attention, patch merging, and spatial…
Does Explainability Transfer? A Controlled Benchmark of Attribution Methods on Vision Transformers and CNNs
Sathiyamohan Nishankar, Nethmi Pathirana, Pubudu Sanjeewani +2
Most evidence on the effectiveness of explainable artificial intelligence (XAI) attribution methods has been established on convolutional neural networks (CNNs), with limited inves…
UtVAA: Ultra-tiny Vision Transformer with Affix Attention for Mobile Image Classification
Romiyal George, Sathiyamohan Nishankar, Selvarajah Thuseethan +1
Vision Transformers (ViTs) have demonstrated strong representation capability in image classification. However, their quadratic self-attention complexity and large parameter counts…
U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition
Romiyal George, Sathiyamohan Nishankar, Selvarajah Thuseethan +4
Federated learning has emerged as a privacy-preserving and efficient approach for deploying intelligent agricultural solutions. Accurate edge-based diagnosis across geographically…