13 citations · 21 across the 8 of their papers we have counts for
5 papers · 1 filter
UbiQVision: Quantifying Uncertainty in XAI for Image Recognition
Akshat Dubey, Aleksandar Anžel, Bahar İlgen +1
Recent advances in deep learning have led to its widespread adoption across diverse domains, including medical imaging. This progress is driven by increasingly sophisticated model…
PepTriX: A Framework for Explainable Peptide Analysis through Protein Language Models
Vincent Schilling, Akshat Dubey, Georges Hattab
Peptide classification tasks, such as predicting toxicity and HIV inhibition, are fundamental to bioinformatics and drug discovery. Traditional approaches rely heavily on handcraft…
UbiQTree: Uncertainty Quantification in XAI with Tree Ensembles
Akshat Dubey, Aleksandar Anžel, Bahar İlgen +1
Explainable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP), have become essential tools for interpreting complex ensemble tree-based models,…
PHAX: A Structured Argumentation Framework for User-Centered Explainable AI in Public Health and Biomedical Sciences
Bahar İlgen, Akshat Dubey, Georges Hattab
Ensuring transparency and trust in AI-driven public health and biomedical sciences systems requires more than accurate predictions-it demands explanations that are clear, contextua…
Surrogate Interpretable Graph for Random Decision Forests
Akshat Dubey, Aleksandar Anžel, Georges Hattab
The field of health informatics has been profoundly influenced by the development of random forest models, which have led to significant advances in the interpretability of feature…