Yuhong Wen
A5047643409- Privacy-Preserving Technologies in Data
- COVID-19 diagnosis using AI
- Stochastic Gradient Optimization Techniques
- Multimodal Machine Learning Applications
- Artificial Intelligence in Healthcare and Education
- Topic Modeling
- Digital Radiography and Breast Imaging
- Cryptography and Data Security
- AI in cancer detection
- Global Cancer Incidence and Screening
- Big Data Technologies and Applications
Nvidia (United States)
2020-2021
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining anonymity, thus removing many barriers to sharing. Here we 20 institutes across the globe train FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts future oxygen requirements of symptomatic patients COVID-19 using inputs vital signs, laboratory and X-rays. achieved an average area under curve (AUC) >0.92 predicting...
Abstract ‘Federated Learning’ (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the thus removing many barriers sharing. During SARS-COV-2 pandemic, 20 institutes collaborated on healthcare FL study predict future oxygen requirements infected patients using inputs vital signs, laboratory data, and chest x-rays, constituting “EXAM” (EMR CXR AI Model) model. EXAM achieved an average Area Under Curve (AUC) over 0.92,...
In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling leveraging data effectively has become a critical challenge. Most state-of-the-art machine learning algorithms are data-centric. However, as lifeblood model performance, necessary cannot always be centralized due to various factors such privacy, regulation, geopolitics, copyright issues, sheer effort required move vast datasets. this paper, we explore how federated enabled by NVIDIA...
Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). is dedicated to implementing a cohesive approach FL, analytics, evaluation. Over time, has cultivated extensive strategies algorithms tailored for FL application development, fostering vibrant community research industry. Conversely, FLARE prioritized the creation an enterprise-ready, resilient runtime environment explicitly...
Detecting clinically relevant objects in medical images is a challenge despite large datasets due to the lack of detailed labels. To address label issue, we utilize scene-level labels with detection architecture that incorporates natural language information. We present challenging new set radiologist paired bounding box and annotations on publicly available MIMIC-CXR dataset especially focussed pneumonia pneumothorax. Along dataset, joint vision weakly supervised transformer layer-selected...