Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
Semi-supervised semantic segmentation (SSSS) aims to improve segmentation performance by utilizing large amounts of unlabeled data with limited labeled samples. Existing methods often suffer from coupling, where over-reliance on initial labeled data leads to suboptimal learning; confirmation bias, where incorrect predictions reinforce themselves repeatedly; and boundary blur caused by limited bo...
Variational inequalities play a pivotal role in a wide array of scientific and engineering applications. This project presents two techniques for adaptive mesh refinement (AMR) in the context of variational inequalities, with a specific focus on the classical obstacle problem. We propose two distinct AMR strategies: Variable Coefficient Elliptic Smoothing (VCES) and Unstructured Dilation Opera...
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...
In recent years, Semantic Communication (SemCom), which aims to achieve efficient and reliable transmission of meaning between agents, has garnered ...
Quantum security improves cryptographic protocols by applying quantum mechanics principles, assuring resistance to both quantum and conventional com...