84 citations · 103 across the 14 of their papers we have counts for
15 papers
Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs
Mahdi Alkaeed, Adnan Qayyum, Nabeel Abo Kashreef +2
Large Language Models (LLMs) are increasingly used in clinical applications. However, their behavior remains highly sensitive to subtle linguistic variations, such as rephrasing or…
Uncovering Privacy Vulnerabilities through Analytical Gradient Inversion Attacks
Tamer Ahmed Eltaras, Qutaibah Malluhi, Alessandro Savino +2
Federated learning has emerged as a prominent privacy-preserving technique for leveraging large-scale distributed datasets by sharing gradients instead of raw data. However, recent…
TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation
Muhammad Bilal, Mahmood Alam, Deepa Bapu +16
Indexing endoscopic surgical videos is vital in surgical data science, forming the basis for systematic retrospective analysis and clinical performance evaluation. Despite its sign…
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription
Mahdi Alkaeed, Sofiat Abioye, Adnan Qayyum +7
In response to the success of proprietary Large Language Models (LLMs), there is a growing interest in developing open, non-proprietary LLMs and AI foundation models (AIFMs) for tr…
Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review
Ufaq Khan, Umair Nawaz, Adnan Qayyum +5
Recent advancements in machine learning (ML) and deep learning (DL), particularly through the introduction of Foundation Models (FMs), have significantly enhanced surgical scene un…
R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional Gradients
Tamer Ahmed Eltaras, Qutaibah Malluhi, Alessandro Savino +3
In the effort to learn from extensive collections of distributed data, federated learning has emerged as a promising approach for preserving privacy by using a gradient-sharing mec…
MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis
Adam Byfield, William Poulett, Ben Wallace +6
Machine learning (ML) models are becoming integral in healthcare technologies, presenting a critical need for formal assurance to validate their safety, fairness, robustness, and t…
Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning
Muhammad Bilal, Dinis Martinho, Reiner Sim +8
Coronary angiography analysis is a common clinical task performed by cardiologists to diagnose coronary artery disease (CAD) through an assessment of atherosclerotic plaque's accum…
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
Shawqi Al-Maliki, Adnan Qayyum, Hassan Ali +5
Deep Neural Networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial…
Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey
Mahdi Alkaeed, Adnan Qayyum, Junaid Qadir
The metaverse is a nascent concept that envisions a virtual universe, a collaborative space where individuals can interact, create, and participate in a wide range of activities. P…
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild
Muhammad Atif Butt, Hassan Ali, Adnan Qayyum +3
Semantic understanding of roadways is a key enabling factor for safe autonomous driving. However, existing autonomous driving datasets provide well-structured urban roads while ign…
Membership Inference Attacks on DNNs using Adversarial Perturbations
Hassan Ali, Adnan Qayyum, Ala Al-Fuqaha +1
Several membership inference (MI) attacks have been proposed to audit a target DNN. Given a set of subjects, MI attacks tell which subjects the target DNN has seen during training.…
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
Adnan Qayyum, Hassan Ali, Massimo Caputo +7
Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emergi…
Can We Revitalize Interventional Healthcare with AI-XR Surgical Metaverses?
Adnan Qayyum, Muhammad Bilal, Muhammad Hadi +5
Recent advancements in technology, particularly in machine learning (ML), deep learning (DL), and the metaverse, offer great potential for revolutionizing surgical science. The com…
Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses
Adnan Qayyum, Muhammad Atif Butt, Hassan Ali +6
Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalised experiences to socialize, work, and play in self-sust…