1 citations · 3 across the 10 of their papers we have counts for
10 papers
Experience with Single Domain Generalization in Real World Medical Imaging Deployments
Ayan Banerjee, Komandoor Srivathsan, Sandeep K. S. Gupta
A desirable property of any deployed artificial intelligence is generalization across domains, i.e. data generation distribution under a specific acquisition condition. In medical…
Hardware Acceleration for Neural Networks: A Comprehensive Survey
Bin Xu, Ayan Banerjee, Sandeep Gupta
Neural networks have become dominant computational workloads across cloud and edge platforms, but their rapid growth in model size and deployment diversity has exposed hardware bot…
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…
Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics
Bin Xu, Ayan Banerjee, Sandeep Gupta
Physical AI at the edge -- enabling autonomous systems to understand and predict real-world dynamics in real time -- requires hardware-efficient learning and inference. Model recov…
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…
Fast Online Digital Twinning on FPGA for Mission Critical Applications
Bin Xu, Ayan Banerjee, Sandeep K. S. Gupta
Digital twinning enables real-time simulation and predictive modeling by maintaining a continuously updated virtual representation of a physical system. In mission-critical applica…
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…
Hardware Software Optimizations for Fast Model Recovery on Reconfigurable Architectures
Bin Xu, Ayan Banerjee, Sandeep Gupta
Model Recovery (MR) is a core primitive for physical AI and real-time digital twins, but GPUs often execute MR inefficiently due to iterative dependencies, kernel-launch overheads,…
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…