8 citations · 11 across the 5 of their papers we have counts for
5 papers
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Ming Li, Pengcheng Xu, Junjie Hu +2
Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not com…
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation
Ming Li, Guang Yang
In medical image analysis, Federated Learning (FL) stands out as a key technology that enables privacy-preserved, decentralized data processing, crucial for handling sensitive medi…
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis
Ming Li, Guang Yang
Thorax disease analysis in large-scale, multi-centre, and multi-scanner settings is often limited by strict privacy policies. Federated learning (FL) offers a potential solution, w…
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels
Ming Li, Yingying Fang, Zeyu Tang +5
The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need f…
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Ming Li, Weiwei Zhang, Guang Yang +5
Multi-view echocardiographic sequences segmentation is crucial for clinical diagnosis. However, this task is challenging due to limited labeled data, huge noise, and large gaps acr…