activity
20172024
most citedThe Future of Digital Health with Federated Learning

3.1k citations · 10k across the 31 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

eess.IV2020★ 38 cited

Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets

Reuben Dorent, Thomas Booth, Wenqi Li +5

Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been de…

cs.CV2020★ 92 cited

Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking

Luis C. García-Peraza-Herrera, Wenqi Li, Caspar Gruijthuijsen +7

Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FC…

cs.CV2020★ 6 cited

LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

Wentao Zhu, Can Zhao, Wenqi Li +3

Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data paralle…

cs.AI2020★ 11 cited

Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA

Xianpei Han, Zhichun Wang, Jiangtao Zhang +22

Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation…

cs.CY2020★ 3.1k cited

The Future of Digital Health with Federated Learning

Nicola Rieke, Jonny Hancox, Wenqi Li +14

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern…