Search Results for author: Adnan Qayyum

Found 18 papers, 3 papers with code

MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis

no code implementations23 Nov 2023 Adam Byfield, William Poulett, Ben Wallace, Anusha Jose, Shatakshi Tyagi, Smita Shembekar, Adnan Qayyum, Junaid Qadir, Muhammad Bilal

Machine learning (ML) models are becoming integral in healthcare technologies, presenting a critical need for formal assurance to validate their safety, fairness, robustness, and trustworthiness.

Fairness Tumour Classification

Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning

no code implementations27 Oct 2023 Muhammad Bilal, Dinis Martinho, Reiner Sim, Adnan Qayyum, Hunaid Vohra, Massimo Caputo, Taofeek Akinosho, Sofiat Abioye, Zaheer Khan, Waleed Niaz, Junaid Qadir

This study introduces an end-to-end machine learning solution developed as part of our solution for the MICCAI 2023 Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs (ARCADE) challenge, which aims to benchmark solutions for multivessel coronary artery segmentation and potential stenotic lesion localisation from X-ray coronary angiograms.

Coronary Artery Segmentation Ensemble Learning +2

Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally

no code implementations5 Oct 2023 Shawqi Al-Maliki, Adnan Qayyum, Hassan Ali, Mohamed Abdallah, Junaid Qadir, Dinh Thai Hoang, Dusit Niyato, Ala Al-Fuqaha

This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating pro-social applications.

Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey

no code implementations19 Sep 2023 Mahdi Alkaeed, Adnan Qayyum, Junaid Qadir

In this paper, we explore various privacy challenges that future metaverses are expected to face, given their reliance on AI for tracking users, creating XR and MR experiences, and facilitating interactions.

Federated Learning Mixed Reality

Membership Inference Attacks on DNNs using Adversarial Perturbations

1 code implementation11 Jul 2023 Hassan Ali, Adnan Qayyum, Ala Al-Fuqaha, Junaid Qadir

Secondly, we utilize the framework to propose two novel attacks: (1) Adversarial Membership Inference Attack (AMIA) efficiently utilizes the membership and the non-membership information of the subjects while adversarially minimizing a novel loss function, achieving 6% TPR on both Fashion-MNIST and MNIST datasets; and (2) Enhanced AMIA (E-AMIA) combines EMIA and AMIA to achieve 8% and 4% TPRs on Fashion-MNIST and MNIST datasets respectively, at 1% FPR.

Inference Attack Membership Inference Attack

Can We Revitalize Interventional Healthcare with AI-XR Surgical Metaverses?

no code implementations25 Mar 2023 Adnan Qayyum, Muhammad Bilal, Muhammad Hadi, Paweł Capik, Massimo Caputo, Hunaid Vohra, Ala Al-Fuqaha, Junaid Qadir

Recent advancements in technology, particularly in machine learning (ML), deep learning (DL), and the metaverse, offer great potential for revolutionizing surgical science.

Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses

no code implementations24 Oct 2022 Adnan Qayyum, Muhammad Atif Butt, Hassan Ali, Muhammad Usman, Osama Halabi, Ala Al-Fuqaha, Qammer H. Abbasi, Muhammad Ali Imran, Junaid Qadir

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-sustaining and hyper-spatio-temporal virtual world(s).

Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

no code implementations19 Jan 2021 Adnan Qayyum, Kashif Ahmad, Muhammad Ahtazaz Ahsan, Ala Al-Fuqaha, Junaid Qadir

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency).

COVID-19 Diagnosis Edge-computing +1

Secure and Robust Machine Learning for Healthcare: A Survey

no code implementations21 Jan 2020 Adnan Qayyum, Junaid Qadir, Muhammad Bilal, Ala Al-Fuqaha

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images.

BIG-bench Machine Learning Privacy Preserving

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward

no code implementations29 May 2019 Adnan Qayyum, Muhammad Usama, Junaid Qadir, Ala Al-Fuqaha

Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services.

Autonomous Vehicles BIG-bench Machine Learning

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