108 citations · 330 across the 34 of their papers we have counts for
7 papers · 1 filter
Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation
Wentao Pan, Jiangpeng Yan, Hanbo Chen +4
Recently, deep neural networks have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole sli…
Distributed Differentially Private Control Synthesis for Multi-Agent Systems with Metric Temporal Logic Specifications
Nasim Baharisangari, Zhe Xu
In this paper, we propose a distributed differentially private receding horizon control (RHC) approach for multi-agent systems (MAS) with metric temporal logic (MTL) specifications…
A Hybrid Tracking Control Strategy for an Unmanned Underwater Vehicle Aided with Bioinspired Neural Dynamics
Zhe Xu, Tao Yan, Simon X. Yang +1
Tracking control has been a vital research topic in robotics. This paper presents a novel hybrid control strategy for an unmanned underwater vehicle (UUV) based on a bioinspired ne…
Non-Parametric Neuro-Adaptive Formation Control
Christos K. Verginis, Zhe Xu, Ufuk Topcu
We develop a learning-based algorithm for the distributed formation control of networked multi-agent systems governed by unknown, nonlinear dynamics. Most existing algorithms eithe…
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
Jiashun Chen, Donghuan Lu, Yu Zhang +5
Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to s…
Consensus Formation Tracking for Multiple AUV Systems Using Distributed Bioinspired Sliding Mode Control
Tao Yan, Zhe Xu, Simon X. Yang
Consensus formation tracking of multiple autonomous underwater vehicles (AUVs) subject to nonlinear and uncertain dynamics is a challenging problem in robotics. To tackle this chal…
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction
Jiangpeng Yan, Chenghui Yu, Hanbo Chen +4
Recently, deep neural networks have greatly advanced undersampled Magnetic Resonance Image (MRI) reconstruction, wherein most studies follow the one-anatomy-one-network fashion, i.…