IViT applies quadratic programming to a pre-trained Vision Transformer with a multi-objective loss, achieving 93.80% accuracy on six skin disease datasets (0.21% below baseline) while reducing feature redundancy by 29.5% and producing clinically consistent activations.
Federated Machine Learning for Detection of Skin Diseases and Enhancement of Internet of Medical Things (IoMT) Security,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Bibliometric and thematic analysis of IoMT research reveals increasing publication trends, similar themes in funded and non-funded papers with funded work emphasizing AI, and a positive association between funded paper counts and health determinants.
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IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection
IViT applies quadratic programming to a pre-trained Vision Transformer with a multi-objective loss, achieving 93.80% accuracy on six skin disease datasets (0.21% below baseline) while reducing feature redundancy by 29.5% and producing clinically consistent activations.
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Scholarly Production and Public Health Determinants in Context of Funding: The Case of IoMT Research:
Bibliometric and thematic analysis of IoMT research reveals increasing publication trends, similar themes in funded and non-funded papers with funded work emphasizing AI, and a positive association between funded paper counts and health determinants.