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Yuhuan Lu - IEEE Xplore Author Profile
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Yuhuan Lu

Publications
13
Citations
93
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (42)
Image of author Yuhuan Lu

Yuhuan Lu

Affiliation

College of Computer Science and Electronic Engineering
Hunan University
Changsha, China

Publication Topics

Biography

Yuhuan Lu received the B.Eng. and M.S. degrees in transportation engineering from Sun Yat-sen University, Guangzhou, China, in 2017 and 2020, respectively. He is currently pursuing the Ph.D. degree with the Department of Computer and Information Science, University of Macau, Macau, and also a Research Assistant with the School of Intelligent Systems Engineering, Sun Yat-sen University. His research interests include graph embedding, urban computing, and intelligent transportation systems.(Based on document published on 24 May 2022).
Publications
13
Citations
93
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (42)
Author's Published Works

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Optical Flow-Enhanced Mamba U-Net for Cardiac Phase Detection in Ultrasound Videos

Yuhuan Lu;Guanghua Tan;Bin Pu;Pak-Hei Yeung;Hang Wang;Shengli Li;Jagath C. Rajapakse;Kenli Li

IEEE Transactions on Medical Imaging
Year: 2025 | Early Access Article |
The detection of cardiac phase in ultrasound videos, identifying end-systolic (ES) and end-diastolic (ED) frames, is a critical step in assessing cardiac function, monitoring structural changes, and diagnosing congenital heart disease. Current popular methods use recurrent neu ral networks to track dependencies over long sequences for cardiac phase detection, but often overlook the short-term moti...Show More

Optical Flow-Enhanced Mamba U-Net for Cardiac Phase Detection in Ultrasound Videos

Yuhuan Lu;Guanghua Tan;Bin Pu;Pak-Hei Yeung;Hang Wang;Shengli Li;Jagath C. Rajapakse;Kenli Li

Year: 2025 | Early Access Article |

AP-Net: Semi-Supervised Ultrasound Cardiac Segmentation Using Enhanced Anatomical Prior

Yuhuan Lu;Jintang Li;Jianxin Lin;Ying Yuan;Jagath C. Rajapakse;Ningbo Zhu;Chunlian Wang;Kenli Li

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Semi-supervised segmentation is gaining popularity in medical image analysis due to challenges in data acquisition and annotation. However, most methods focus on generating additional training pairs from unlabeled data through augmentation or perturbation for contrastive learning, often overlooking the unique characteristics and inherent priors of medical images. We identified two key anatomical p...Show More

AP-Net: Semi-Supervised Ultrasound Cardiac Segmentation Using Enhanced Anatomical Prior

Yuhuan Lu;Jintang Li;Jianxin Lin;Ying Yuan;Jagath C. Rajapakse;Ningbo Zhu;Chunlian Wang;Kenli Li

Year: 2025 | Conference Paper |

Lane Change Prediction for Autonomous Driving With Transferred Trajectory Interaction

Yuhuan Lu;Pengpeng Xu;Xinyu Jiang;Ali Kashif Bashir;Thippa Reddy Gadekallu;Wei Wang;Xiping Hu

IEEE Transactions on Intelligent Transportation Systems
Year: 2025 | Early Access Article |
In mixed-autonomy traffic environments, accurately predicting the lane change behavior of human-driven vehicles is critical for ensuring the safety and reliability of autonomous vehicle decision-making. However, existing approaches face two major challenges: 1) they tend to represent the relationships between the target vehicle and surrounding vehicles using parameters like relative position and s...Show More

Lane Change Prediction for Autonomous Driving With Transferred Trajectory Interaction

Yuhuan Lu;Pengpeng Xu;Xinyu Jiang;Ali Kashif Bashir;Thippa Reddy Gadekallu;Wei Wang;Xiping Hu

Year: 2025 | Early Access Article |

Knowledge-Driven Lane Change Prediction for Secure and Reliable Internet of Vehicles

Yuhuan Lu;Zhen Zhang;Wei Wang;Yiting Zhu;Tiantian Chen;Yasser D. Al-Otaibi;Ali Kashif Bashir;Xiping Hu

IEEE Transactions on Intelligent Transportation Systems
Year: 2025 | Early Access Article |
Ensuring the smooth operation of road traffic is a momentous target in Intelligent Transportation Systems, which can be expedited by a secure and reliable Internet of Vehicles (IoV). As prominent carriers of the IoV, intelligent vehicles (IVs), that bear the promising potential for alleviating traffic congestion, have become the core road traffic participants. However, the mixed-traffic environmen...Show More

Knowledge-Driven Lane Change Prediction for Secure and Reliable Internet of Vehicles

Yuhuan Lu;Zhen Zhang;Wei Wang;Yiting Zhu;Tiantian Chen;Yasser D. Al-Otaibi;Ali Kashif Bashir;Xiping Hu

Year: 2025 | Early Access Article |

Inter-Intra Cluster Reorganization for Unsupervised Vehicle Re-Identification

Mingkai Qiu;Yuhuan Lu;Xiying Li;Qiang Lu

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 12 | Journal Article |
State-of-the-art unsupervised object re-identification (Re-ID) methods conduct model training with pseudo labels generated by clustering techniques. Unfortunately, due to the existence of inter-ID similarity and intra-ID variance problems in vehicle Re-ID, clustering sometimes mixes different similar vehicles together or splits images of the same vehicle in different views into different clusters....Show More

Inter-Intra Cluster Reorganization for Unsupervised Vehicle Re-Identification

Mingkai Qiu;Yuhuan Lu;Xiying Li;Qiang Lu

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

SKGC: A General Semantic-Level Knowledge Guided Classification Framework for Fetal Congenital Heart Disease

Yuhuan Lu;Guanghua Tan;Bin Pu;Hang Wang;Bocheng Liang;Kenli Li;Jagath C. Rajapakse

IEEE Journal of Biomedical and Health Informatics
Year: 2024 | Volume: 28, Issue: 10 | Journal Article |
Cited by: Papers (1)
Congenital heart disease (CHD) is the most common congenital disability affecting healthy development and growth, even resulting in pregnancy termination or fetal death. Recently, deep learning techniques have made remarkable progress to assist in diagnosing CHD. One very popular method is directly classifying fetal ultrasound images, recognized as abnormal and normal, which tends to focus more on...Show More

SKGC: A General Semantic-Level Knowledge Guided Classification Framework for Fetal Congenital Heart Disease

Yuhuan Lu;Guanghua Tan;Bin Pu;Hang Wang;Bocheng Liang;Kenli Li;Jagath C. Rajapakse

Year: 2024 | Volume: 28, Issue: 10 | Journal Article |

Camera-Aware Differentiated Clustering With Focal Contrastive Learning for Unsupervised Vehicle Re-Identification

Mingkai Qiu;Yuhuan Lu;Xiying Li;Qiang Lu

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 10 | Journal Article |
Most existing research on vehicle re-identification (Re-ID) focuses on supervised methods, while unsupervised methods that can take advantage of massive unlabeled data are underexplored. Due to the similarity of tasks, unsupervised person Re-ID methods that employ clustering to generate pseudo labels for model training can achieve good performance on unsupervised vehicle Re-ID task. However, vehic...Show More

Camera-Aware Differentiated Clustering With Focal Contrastive Learning for Unsupervised Vehicle Re-Identification

Mingkai Qiu;Yuhuan Lu;Xiying Li;Qiang Lu

Year: 2024 | Volume: 34, Issue: 10 | Journal Article |

HFSCCD: A Hybrid Neural Network for Fetal Standard Cardiac Cycle Detection in Ultrasound Videos

Bin Pu;Kenli Li;Jianguo Chen;Yuhuan Lu;Qing Zeng;Jiewen Yang;Shengli Li

IEEE Journal of Biomedical and Health Informatics
Year: 2024 | Volume: 28, Issue: 5 | Journal Article |
Cited by: Papers (7)
In the fetal cardiac ultrasound examination, standard cardiac cycle (SCC) recognition is the essential foundation for diagnosing congenital heart disease. Previous studies have mostly focused on the detection of adult CCs, which may not be applicable to the fetus. In clinical practice, localization of SCCs needs to recognize end-systole (ES) and end-diastole (ED) frames accurately, ensuring that e...Show More

HFSCCD: A Hybrid Neural Network for Fetal Standard Cardiac Cycle Detection in Ultrasound Videos

Bin Pu;Kenli Li;Jianguo Chen;Yuhuan Lu;Qing Zeng;Jiewen Yang;Shengli Li

Year: 2024 | Volume: 28, Issue: 5 | Journal Article |

A Fast Hybrid Feature Selection Method Based on Dynamic Clustering and Improved Particle Swarm Optimization for High-Dimensional Health Care Data

Yan Kang;Luhan Peng;Jing Guo;Yuhuan Lu;Yun Yang;Baochen Fan;Bin Pu

IEEE Transactions on Consumer Electronics
Year: 2024 | Volume: 70, Issue: 1 | Journal Article |
Cited by: Papers (1)
The ubiquity and commoditization of wearable sensors have generated a deluge of user-generated health care data and played a key role in clinical utility, particularly when incorporated into personalized prediction models. The “curse of dimensionality” and enormous computational costs are still the main challenges faced by the existing algorithms as the number of wearable datasets exponentially in...Show More

A Fast Hybrid Feature Selection Method Based on Dynamic Clustering and Improved Particle Swarm Optimization for High-Dimensional Health Care Data

Yan Kang;Luhan Peng;Jing Guo;Yuhuan Lu;Yun Yang;Baochen Fan;Bin Pu

Year: 2024 | Volume: 70, Issue: 1 | Journal Article |

Schema-Aware Hyper-Relational Knowledge Graph Embeddings for Link Prediction

Yuhuan Lu;Dingqi Yang;Pengyang Wang;Paolo Rosso;Philippe Cudre-Mauroux

IEEE Transactions on Knowledge and Data Engineering
Year: 2024 | Volume: 36, Issue: 6 | Journal Article |
Cited by: Papers (1)
Knowledge Graph (KG) embeddings have become a powerful paradigm to resolve link prediction tasks for KG completion. The widely adopted triple-based representation, where each triplet $(h,r,t)$(h,r,t) links two entities $h$h and $t$t through a relation $r$r, oversimplifies the complex nature of the data stored in a KG, in particular for hyper-relational facts, where each fact contains not only a ba...Show More

Schema-Aware Hyper-Relational Knowledge Graph Embeddings for Link Prediction

Yuhuan Lu;Dingqi Yang;Pengyang Wang;Paolo Rosso;Philippe Cudre-Mauroux

Year: 2024 | Volume: 36, Issue: 6 | Journal Article |

A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images

Yuhuan Lu;Kenli Li;Bin Pu;Ying Tan;Ningbo Zhu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Year: 2024 | Volume: 21, Issue: 4 | Journal Article |
Cited by: Papers (7)
Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key anatomical structures in a four-chamber view is an essential step in measuring fetal growth parameters and diagnosing CHD. Currently, most obstetricians perform segmentation tasks manually, but the pixel-level operation is labor-intensive and require...Show More

A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images

Yuhuan Lu;Kenli Li;Bin Pu;Ying Tan;Ningbo Zhu

Year: 2024 | Volume: 21, Issue: 4 | Journal Article |

Vehicle Trajectory Prediction in Connected Environments via Heterogeneous Context-Aware Graph Convolutional Networks

Yuhuan Lu;Wei Wang;Xiping Hu;Pengpeng Xu;Shengwei Zhou;Ming Cai

IEEE Transactions on Intelligent Transportation Systems
Year: 2023 | Volume: 24, Issue: 8 | Journal Article |
Cited by: Papers (28)
The accurate trajectory prediction of surrounding vehicles is crucial for the sustainability and safety of connected and autonomous vehicles under mixed traffic streams in the real world. The task of trajectory prediction is challenging because there are all kinds of factors affecting the motions of vehicles, such as the individual movements, the ambient driving environment especially road conditi...Show More

Vehicle Trajectory Prediction in Connected Environments via Heterogeneous Context-Aware Graph Convolutional Networks

Yuhuan Lu;Wei Wang;Xiping Hu;Pengpeng Xu;Shengwei Zhou;Ming Cai

Year: 2023 | Volume: 24, Issue: 8 | Journal Article |

MobileUNet-FPN: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber Segmentation in Edge Computing Environments

Bin Pu;Yuhuan Lu;Jianguo Chen;Shengli Li;Ningbo Zhu;Wei Wei;Kenli Li

IEEE Journal of Biomedical and Health Informatics
Year: 2022 | Volume: 26, Issue: 11 | Journal Article |
Cited by: Papers (48)
The apical four-chamber (A4C) view in fetal echocardiography is a prenatal examination widely used for the early diagnosis of congenital heart disease (CHD). Accurate segmentation of A4C key anatomical structures is the basis for automatic measurement of growth parameters and necessary disease diagnosis. However, due to the ultrasound imaging arising from artefacts and scattered noise, the variabi...Show More

MobileUNet-FPN: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber Segmentation in Edge Computing Environments

Bin Pu;Yuhuan Lu;Jianguo Chen;Shengli Li;Ningbo Zhu;Wei Wei;Kenli Li

Year: 2022 | Volume: 26, Issue: 11 | Journal Article |

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