Di Miao

SimuFreeMark: A Noise-Simulation-Free Robust Watermarking Against Image Editing

Yichao Tang, Mingyang Li, Di Miao, Sheng Li et autres

The advancement of artificial intelligence generated content (AIGC) has created a pressing need for robust image watermarking that can withstand both conventional signal processing and novel semantic editing attacks. Current deep learning-based methods rely on training with hand-crafted noise simulation layers, which …

Developing federated time-to-event scores using heterogeneous real-world survival data

Ziwen Wang, Yuqing Shang, Qiming Wu, Chuan Hong et autres

OBJECTIVE: Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain disease or death) for patients is crucial for clinical decision-making. Scoring systems are widely …

sg, us (code pays fourni par la source)

FairFML: fair federated machine learning with a case study on reducing gender disparities in cardiac arrest outcome prediction

Siqi Li, Qiming Wu, Xin Li, Di Miao et autres

Health equity is a critical concern in clinical research and practice, as biased predictive models can exacerbate disparities in clinical decision-making and patient outcomes. As healthcare systems increasingly rely on data-driven models, ensuring fairness in these systems is essential to prevent perpetuating …

sg, us, jp (code pays fourni par la source)

FairFML: A Unified Approach to Algorithmic Fair Federated Learning with Applications to Reducing Gender Disparities in Cardiac Arrest Outcomes

Siqi Li, Di Miao, Chuan Hong, Yilin Ning et autres

Addressing algorithmic bias in healthcare is crucial for ensuring equity in patient outcomes, particularly in cross-institutional collaborations where privacy constraints often limit data sharing. Federated learning (FL) offers a solution by enabling institutions to collaboratively train models without sharing sensitive data, but …

sg, us (code pays fourni par la source)

A scoping review and evidence gap analysis of clinical AI fairness

M. Liu, Yilin Ning, Salinelat Teixayavong, Xiaoxuan Liu et autres

The ethical integration of artificial intelligence (AI) in healthcare necessitates addressing fairness. AI fairness involves mitigating biases in AI and leveraging AI to promote equity. Despite advancements, significant disconnects persist between technical solutions and clinical applications. Through evidence gap analysis, this review …

sg, gb, be, us (code pays fourni par la source)

Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis

Siqi Li, Xin Li, Kunyu Yu, Qiming Wu et autres

Background: Clinical and biomedical research in low-resource settings often faces substantial challenges due to the need for high-quality data with sufficient sample sizes to construct effective models. These constraints hinder robust model training and prompt researchers to seek methods for leveraging existing …

sg, us, cn (code pays fourni par la source)

Tailoring enzyme modified starch through high-moisture extrusion: Unraveling structure-property relationships

Xuan Wang, Ning Wang, Jingqi Dai, Di Miao et autres

cn (code pays fourni par la source)

Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist

Yilin Ning, Salinelat Teixayavong, Yuqing Shang, Julian Savulescu et autres

The widespread use of Chat Generative Pre-trained Transformer (known as ChatGPT) and other emerging technology that is powered by generative artificial intelligence (GenAI) has drawn attention to the potential ethical issues they can cause, especially in high-stakes applications such as health care, …

sg, gb, be, cn, us (code pays fourni par la source)

Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data

Siqi Li, Yuqing Shang, Ziwen Wang, Qiming Wu et autres

Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain disease or death) for patients is crucial for clinical decision-making. Scoring systems are widely used …

Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture

Zhen Ling Teo, Liyuan Jin, Nan Liu, Siqi Li et autres

Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture

Zhen Ling Teo, Liyuan Jin, Nan Liu, Siqi Li et autres

Federated learning (FL) is a distributed machine learning framework that is gaining traction in view of increasing health data privacy protection needs. By conducting a systematic review of FL applications in healthcare, we identify relevant articles in scientific, engineering, and medical journals …

sg (code pays fourni par la source)

Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis

Siqi Li, Di Miao, Qiming Wu, Chuan Hong et autres

Background: Federated learning (FL) holds promise for safeguarding data privacy in healthcare collaborations. While the term “FL” was originally coined by the engineering community, the statistical field has also developed privacy-preserving algorithms, though these are less recognized. Our goal was to bridge …

sg, us, gb (code pays fourni par la source)