Author name cluster

Zhicheng Liu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers

2 author rows

JBHI Journal 2026 Journal Article

Self-Supervised X-Ray Coronary Angiography Segmentation with Vessel-Aware Synthesis Learning

Shuang Liang
Zhicheng Liu
Guangyuan Liu
Tianliang Yao
Chunyi Yang
Peng Qi

Percutaneous coronary intervention (PCI) re quires precise, real-time coronary vessel segmentation to ensure procedural safety and efficacy. Self-supervised vascular segmentation technology has attracted widespread attention because it does not rely on artificial standards and has good segmentation effects. However, existing self supervised methods focus primarily on the topological structure of vessels, neglecting texture and geometric de tails. To address this, this paper uses mixed data of real information and synthetic information for training, and simulates the relationship between the two brightness attenuation values of X-rays after passing through the background and blood vessels respectively during the data mixing process. This method utilizes vessel texture synthesis and geometric contour bending in self-supervised vesselseg mentation, and more accurately simulates the relationship between the brightness attenuation of X-rays after passing through the background and blood vessels respectively through the product of ray attenuation rates (instead of the traditional attenuation value addition), thereby solving the object model defects of the existing self-supervised vessel segmentation framework. Experiments have shown that Vessel Aware Synthesis Learning can significantly re duce segmentation errors (Jaccard increased by 9. 6%) and capture more vascular details (Recall increased by 13. 8%). The proposed method can effectively eliminate the reliance on labor-intensive manual annotations. This development improves AI-driven medical image analysis and offers a practical approach to reducing the cost of creating reliable AI tools for medical imaging by decreasing the need for extensive annotations. Source code is available at https://github.com/bigOrange123456789/SSCVS.git.

Details DOI

IROS Conference 2025 Conference Paper

A Novel LiDAR Odometry Based on Surface Distributed Point Feature with Dual Feature Fusion

Jianjie Li
Peiyu Guan
Xurong Gong
Zhicheng Liu
Zhiqiang Cao 0002

LiDAR odometry has gained popularity due to the LiDAR sensor’s accurate depth measurement and robustness to varying illumination conditions. The feature-based methods achieve the advantage of efficiency through feature extraction, while the distribution-based ones attain better accuracy by modeling the point cloud as distributions. Combining the strengths of both methods is anticipated to yield superior performance. However, existing combination schemes typically extract and process features and distributions separately, and such a loosely integrated approach cannot fully leverage their complementarity. To address this problem, we propose a novel LiDAR odometry method based on surface distributed point (SDP) feature with dual feature fusion. Specifically, the SDP feature is introduced to tightly integrate features and distributions, facilitating efficient feature association and map maintenance. On this basis, the associated source and target features are then effectively integrated through dual feature fusion to form the dual-fusion (DF) associated plane. This plane serves as the basis for constructing the point-to-DF associated plane constraint for pose optimization. As a result, the local planar structure is more accurately reflected, thereby enhancing the accuracy of pose estimation. The SDP feature and the resultant constraint are employed in both scan-to-map matching and fixed-lag smoothing, which are hierarchically organized to achieve accurate pose estimation. Experiments on KITTI dataset and large-scale KITTI-360 dataset demonstrate the effectiveness of the proposed method.

Details

IJCAI Conference 2024 Conference Paper

From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas

Yanxin Xi
Yu Liu
Zhicheng Liu
Sasu Tarkoma
Pan Hui
Yong Li

The Sustainable Development Goals (SDGs) aim to resolve societal challenges, such as eradicating poverty and improving the lives of vulnerable populations in impoverished areas. Those areas rely on road infrastructure construction to promote accessibility and economic development. Although publicly available data like OpenStreetMap is available to monitor road status, data completeness in impoverished areas is limited. Meanwhile, the development of deep learning techniques and satellite imagery shows excellent potential for earth monitoring. To tackle the challenge of road network assessment in impoverished areas, we develop a systematic road extraction framework combining an encoder-decoder architecture and morphological operations on satellite imagery, offering an integrated workflow for interdisciplinary researchers. Extensive experiments of road network extraction on real-world data in impoverished regions achieve a 42. 7% enhancement in the F1-score over the baseline methods and reconstruct about 80% of the actual roads. We also propose a comprehensive road network dataset covering approximately 794, 178 km2 area and 17. 048 million people in 382 impoverished counties in China. The generated dataset is further utilized to conduct socioeconomic analysis in impoverished counties, showing that road network construction positively impacts regional economic development. The technical appendix, code, and generated dataset can be found at https: //github. com/tsinghua-fib-lab/Road_network_extraction_impoverished_counties.

PDF Details DOI

TCS Journal 2024 Journal Article

Two-stage BP maximization under p-matroid constraint

Hong Chang
Jing Jin
Zhicheng Liu
Donglei Du
Xiaoyan Zhang

The BP problem maximizes the sum of a suBmodular function and a suPermodular function(BP) subject to some constraints, where both functions are nonnegative and monotonic. This problem has been widely studied under the single-stage setting. In this paper, we consider a variant of the BP maximization problem. The problem is a two-stage BP maximization problem subject to a p-matroid constraint, for which we propose an approximation algorithm with constant approximation ratio parameterized by the curvatures of the two functions involved.

Details DOI

TCS Journal 2023 Journal Article

Two-stage non-submodular maximization

Hong Chang
Jing Jin
Zhicheng Liu
Ping Li
Xiaoyan Zhang

The sheer size of modern datasets has led to an urgent need for summarization techniques that can identify representative elements of the data set. Fortunately, the vast majority of data summarization tasks satisfy an intuitive diminishing returns condition known as submodularity, which allows us to find nearly-optimal solutions in linear time. However, for many applications in practice, including experimental design and sparse Gaussian processes, the objective is in general not submodular. To solve these optimization problems, an important research method is to describe the characteristics of the non-submodular functions. The non-submodular function is a hot research topic in the study of nonlinear combinatorial optimizations. In this paper, we combine and generalize the curvature and the generic submodularity ratio to design an approximation algorithm for two-stage non-submodular maximization under a matroid constraint.

Details DOI

TCS Journal 2022 Journal Article

Improved algorithms for non-submodular function maximization problem

Zhicheng Liu
Jing Jin
Hong Chang
Donglei Du
Xiaoyan Zhang

The concept of submodularity finds wide applications in data science, artificial intelligence, and machine learning, providing a boost to the investigation of new ideas, innovative techniques, and creative algorithms to solve different submodular optimization problems arising from a diversity of applications. However pure submodular or supermodular problems only represent a small portion of the problems we are facing in real life applications. The main focus of this work is to consider a non-submodular function maximization problem subject to a cardinality constraint, where the objective function is the sum of a monotone γ-weakly submodular function and a supermodular function. This problem includes some previously studied problems as special cases, such as the submodular+supermodular maximization problem when γ=1, and the γ-weakly submodular function maximization problem when the supermodular function is void. We present greedy algorithms for this generalized problem under both offline and streaming models, improving existing results.

Details DOI

TCS Journal 2021 Journal Article

Online algorithms for BP functions maximization

Zhicheng Liu
Ling Chen
Hong Chang
Donglei Du
Xiaoyan Zhang

BP maximization problem has many applications in machine learning and data science. It can be described as maximizing the sum of a suBmodular function and a suPermodular function (BP) under some constraints, where both functions are nonnegative and monotonic. In this paper, we consider two online cases. The first is a BP maximization problem subject to a uniform matroid constraint when the items arrive one-by-one, for which we offer an online algorithm with constant competitive ratio. The second is a BP maximization problem subject to a partition matroid constraint where each part of the partition arrives in a random order, for which we present two approximation algorithms of both constant competitive ratios, where one is randomized and the other is deterministic.

Details DOI

ICRA Conference 2020 Conference Paper

A Compact and Low-cost Robotic Manipulator Driven by Supercoiled Polymer Actuators

Yang Yang 0031
Zhicheng Liu
Yanhan Wang
Shuai Liu
Michael Yu Wang

The supercoiled polymer (SCP) actuator is a novel artificial muscle, which is manufactured by twisting and coiling polymer fibers. This new artificial muscle is soft, low-cost and shows good linearity. Being utilized as an actuator, the artificial muscle could generate significant mechanical power in a muscle-like form upon electrical activation by Joule heating. In this study, we adopt this new artificial muscle to actuate a novel designed robotic manipulator, which is composed of two parts. The first part is a robotic arm based on the inspiration of the musculoskeletal system. The arm is fabricated with two ball-and-socket joints as skeleton and SCP actuators as driven muscles. The second part is a Fin Ray Effect inspired soft gripper that can perform grasping tasks on fragile objects. The manipulator prototype is fabricated and experimental tests are conducted including both simple but effective control of the bio-inspired arm as well as characterization of the gripper. Lastly, a pick and place demonstration of a fragile fruit is performed utilizing the proposed manipulator. We envision that the bio-inspired robotic manipulator design driven by SCP actuators could potentially be used in other robotic applications.

Details

AAAI Conference 2020 Conference Paper

Learning Geo-Contextual Embeddings for Commuting Flow Prediction

Zhicheng Liu
Fabio Miranda
Weiting Xiong
Junyan Yang
Qiao Wang
Claudio Silva

Predicting commuting ﬂows based on infrastructure and landuse information is critical for urban planning and public policy development. However, it is a challenging task given the complex patterns of commuting ﬂows. Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios where many factors need to be considered. Meanwhile, most existing machine learning-based methods ignore the spatial correlations and fail to model the inﬂuence of nearby regions. To address these issues, we propose Geocontextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting ﬂow prediction. Speciﬁcally, we ﬁrst construct a geo-adjacency network containing the geographic contextual information. Then, an attention mechanism is proposed based on the framework of graph attention network (GAT) to capture the spatial correlations and encode geographic contextual information to embedding space. Two separate GATs are used to model supply and demand characteristics. To enhance the effectiveness of the embedding representation, a multitask learning framework is used to introduce stronger restrictions, forcing the embeddings to encapsulate effective representation for ﬂow prediction. Finally, a gradient boosting machine is trained based on the learned embeddings to predict commuting ﬂows. We evaluate our model using real-world dataset from New York City and the experimental results demonstrate the effectiveness of our proposed method against the state of the art.

PDF Details