8 citations · 11 across the 6 of their papers we have counts for
6 papers
Label-efficient multi-organ segmentation with a diffusion model
Yongzhi Huang, Fengjun Xi, Liyun Tu +7
Accurate segmentation of multiple organs in Computed Tomography (CT) images plays a vital role in computer-aided diagnosis systems. While various supervised learning approaches hav…
A Comparative Study of Pre-trained CNNs and GRU-Based Attention for Image Caption Generation
Rashid Khan, Bingding Huang, Haseeb Hassan +2
Image captioning is a challenging task involving generating a textual description for an image using computer vision and natural language processing techniques. This paper proposes…
Hybrid of representation learning and reinforcement learning for dynamic and complex robotic motion planning
Chengmin Zhou, Xin Lu, Jiapeng Dai +3
Motion planning is the soul of robot decision making. Classical planning algorithms like graph search and reaction-based algorithms face challenges in cases of dense and dynamic ob…
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review
Chengmin Zhou, Chao Wang, Haseeb Hassan +3
Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees o…
3D Cross-Pseudo Supervision (3D-CPS): A semi-supervised nnU-Net architecture for abdominal organ segmentation
Yongzhi Huang, Hanwen Zhang, Yan Yan +1
Large curated datasets are necessary, but annotating medical images is a time-consuming, laborious, and expensive process. Therefore, recent supervised methods are focusing on util…
A review of motion planning algorithms for intelligent robotics
Chengmin Zhou, Bingding Huang, Pasi Fränti
We investigate and analyze principles of typical motion planning algorithms. These include traditional planning algorithms, supervised learning, optimal value reinforcement learnin…