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Found 18 papers, 3 papers with code
MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis
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.
Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning
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.
Ranked #3 on
Coronary Artery Segmentation
on ARCADE
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
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
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.
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild
Semantic understanding of roadways is a key enabling factor for safe autonomous driving.
Membership Inference Attacks on DNNs using Adversarial Perturbations
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.
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
In this paper, we propose a systematic methodology for developing robust models for surgical tool detection using noisy data.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
Can We Revitalize Interventional Healthcare with AI-XR Surgical Metaverses?
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
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
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).
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification
In recent years, deep learning (DL) techniques have provided state-of-the-art performance on different medical imaging tasks.
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals.
Secure and Robust Machine Learning for Healthcare: A Survey
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.
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
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.
Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
Cross-lingual speech emotion recognition is an important task for practical applications.
Medical Image Analysis using Convolutional Neural Networks: A Review
Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features.
Medical Image Retrieval using Deep Convolutional Neural Network
The learned features and the classification results are used to retrieve medical images.
