activity
20172021
most citedGeneralised Dice overlap as a deep learning loss function for highly unbalanced segmentations

2.7k citations · 3.3k across the 9 of their papers we have counts for

collaborators

20 papers

cs.CV2021

Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation

Jingkun Chen, Wenqi Li, Hongwei Li +1

Multi-modal medical image segmentation plays an essential role in clinical diagnosis. It remains challenging as the input modalities are often not well-aligned spatially. Existing…

eess.IV2020

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.CV202092 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.CV20206 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

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

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…