Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
Over the past decade, the Table Union Search (TUS) task has aimed to identify unionable tables within data lakes to improve data integration and discovery. While numerous solutions and approaches have been introduced, they primarily rely on open data, making them not applicable to restricted access data, such as medical records or government statistics, due to privacy concerns. Restricted data c...
Fast and accurate estimation of quantiles on data streams coming from communication networks, Internet of Things (IoT), and alike, is at the heart of important data processing applications including statistical analysis, latency monitoring, query optimization for parallel database management systems, and more. Indeed, quantiles are more robust indicators for the underlying distribution, compared...
Semi-supervised semantic segmentation (SSSS) aims to improve segmentation performance by utilizing large amounts of unlabeled data with limited labe...
Variational inequalities play a pivotal role in a wide array of scientific and engineering applications. This project presents two techniques for ad...
Medical image segmentation plays a crucial role in various clinical applications. A major challenge in medical image segmentation is achieving accur...
Finding the cadastral boundaries of farmlands is a crucial concern for land administration. Therefore, using deep learning methods to expedite and s...
Existing supervised action segmentation methods depend on the quality of frame-wise classification using attention mechanisms or temporal convolutio...
The deployment of advanced deep learning models for medical image segmentation is often constrained by the requirement for extensively annotated dat...
Despite the rapid development of safety alignment techniques for LLMs, defending against multi-turn jailbreaks is still a challenging task. In this ...
Resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity networks (FCNs) have become critical for under...
Immersed boundary methods have attracted substantial interest in the last decades due to their potential for computations involving complex geometri...
This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and...
This work presents a new framework for approximating Caputo fractional derivatives (FDs) of any positive order using a shifted Gegenbauer pseudospec...
Secure Multi-Party Computation (MPC) is an important enabling technology for data privacy in modern distributed applications. We develop a new type ...
Automated diagnostic systems (ADS) have shown significant potential in the early detection of polyps during endoscopic examinations, thereby reducin...
Surface parametrization is a crucial part in various fields, having applications in computer graphic, medical imaging, scientific computing and comp...
Large language models (LLMs) have emerged as powerful tools in natural language processing (NLP), showing a promising future of artificial generated...
Delineating farmland boundaries is essential for agricultural management such as crop monitoring and agricultural census. Traditional methods using ...
Monte-Carlo (MC) Dropout provides a practical solution for estimating predictive distributions in deterministic neural networks. Traditional dropout...
Large Language Models (LLMs) hold promise for advancing legal practice by automating complex tasks and improving access to justice. However, their a...