5 papers
XAI-MeD: Explainable Knowledge Guided Neuro-Symbolic Framework for Domain Generalization and Rare Class Detection in Medical Imaging
Midhat Urooj, Ayan Banerjee, Sandeep Gupta
Explainability domain generalization and rare class reliability are critical challenges in medical AI where deep models often fail under real world distribution shifts and exhibit…
NEURO-GUARD: Neuro-Symbolic Generalization and Unbiased Adaptive Routing for Diagnostics -- Explainable Medical AI
Midhat Urooj, Ayan Banerjee, Sandeep Gupta
Accurate yet interpretable image-based diagnosis remains a central challenge in medical AI, particularly in settings characterized by limited data, subtle visual cues, and high-sta…
Accelerated Digital Twin Learning for Edge AI: A Comparison of FPGA and Mobile GPU
Bin Xu, Ayan Banerjee, Midhat Urooj +1
Digital twins (DTs) can enable precision healthcare by continually learning a mathematical representation of patient-specific dynamics. However, mission critical healthcare applica…
MedXAI: A Retrieval-Augmented and Self-Verifying Framework for Knowledge-Guided Medical Image Analysis
Midhat Urooj, Ayan Banerjee, Farhat Shaikh +2
Accurate and interpretable image-based diagnosis remains a fundamental challenge in medical AI, particularly under domain shifts and rare-class conditions. Deep learning models oft…
Single Domain Generalization in Diabetic Retinopathy: A Neuro-Symbolic Learning Approach
Midhat Urooj, Ayan Banerjee, Farhat Shaikh +2
Domain generalization remains a critical challenge in medical imaging, where models trained on single sources often fail under real-world distribution shifts. We propose KG-DG, a n…