Author name cluster

Wendong Wang

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7 papers

2 author rows

JBHI Journal 2026 Journal Article

BECM-Net: A Multi-granularity Collaborative Framework for Semi-Supervised Fetal Ultrasound Segmentation

Wei Hu
Cong Tan
Wendong Wang
Zeheng Wang
Qibing Qin
Wenfeng Zhang
Haibo Ni

Accurate segmentation of fetal ultrasound (US) images is essential for measuring the Angle of Progression (AoP) and assessing fetal head descent during labor. However, conventional semi-supervised learning (SSL) for ultrasound segmentation is challenged by inaccurate pseudo-labeling at blurred or low-contrast boundaries and by limited enforcement of consistency. To address these challenges, we propose the Boundary-Enhanced Collaborative Multi-granularity Network (BECM-Net), which, from a multi-granularity modeling perspective, can be interpreted as a unified framework that jointly optimizes pixel-level, region-level, and structure-level representations. Specifically, at the pixel level, a novel DirDiff-Conv module enhances boundary perception and texture representation through multi-orientation differential filtering, enabling fine-grained modeling of local structures. At the region level, the Uncertainty-Confidence Aligned Mix (UCA-Mix) strategy performs uncertainty-guided bidirectional region-level mixing, facilitating semantic alignment and reducing pseudo-label noise. At the structure level, the ContourRefine branch models object contours by integrating deep semantic features with shallow boundary cues while coupling boundary learning with pseudo-label supervision, thereby enforcing structural-level consistency in global shape and boundary continuity. Through collaborative optimization across multiple granularities, BECM-Net provides more reliable supervision and robust feature learning under limited annotations. Extensive experiments on fetal ultrasound datasets demonstrate that BECM-Net can achieve the state-of-the-art performance, with particularly notable gains in challenging regions with ambiguous pubic symphysis and fetal head boundaries.

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AAAI Conference 2026 Conference Paper

Learning to LEAP: Efficient Dense Point Tracking by Focusing Where It Matters

Chenzhi Zhao
Wufan Wang
Bo Zhang
Wendong Wang

Tracking Any Point (TAP) is a foundational task in computer vision with broad applicability. The state-of-the-art self-supervised TAP method leverages a global matching transformer and contrastive random walks to learn point correspondences. However, its dense all-pairs attention and correlation volume computation tend to introduce irrelevant features and produce less informative training signals, degrading both learning efficiency and tracking accuracy. To address these limitations, we introduce LEAP-Track, a self-supervised TAP approach that computes the attention matrices and correlation volume over adaptively selected sparse pairs. It consists of two core designs: (1) Curriculum-based Sparse Attention (CSA), which dynamically focuses on the most relevant keys, promoting the learning of discriminative features; and (2) Progressive k-NN Transition (PkT), which reformulates the contrastive random walk to operate on an increasingly sparse k-NN affinity graph to reinforce the learning of the most informative correspondences. By integrating the above two designs into a two-stage training paradigm, LEAP-Track is shown both theoretically and empirically to effectively boost learning efficiency, achieving superior tracking accuracy over existing self-supervised TAP methods.

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EAAI Journal 2026 Journal Article

Research on multivariate time series prediction method for upper motion intention perception

Yang Meng
Shuhao Liang
Jinda Wang
Fei Niu
Wendong Wang
Zelin Ci

To address the limitations of relying on a single information source and the low accuracy in upper limb motion intention perception during exoskeleton-based rehabilitation training, a multivariate time-series prediction method that integrates a cross-graph convolution module with a stochastic synthetic attention mechanism is proposed. Specifically, a cross-graph convolution module based on Spatial Node Encoding (SNE) is developed to fuse data from the Inertial Measurement Unit (IMU) and visual signals, thereby capturing spatial relationships among variables. A multi - view topology mapping network with a stochastic synthetic attention mechanism is introduced to extract temporal features, and a Graph Convolutional Network - Long Short - Term Memory (GCN - LSTM) model is constructed. The proposed GCN - LSTM model is compared with the 1 - Dimensional Convolution - Long Short - Term Memory (1DConv - LSTM) and Bidirectional Long Short - Term Memory (Bi - LSTM) models through experiments. The results show that the GCN - LSTM achieves a joint trajectory fitting degree, R2, of 0. 9417. It represents an approximate 9 % improvement over 1DConv–LSTM and a 10 % improvement over Bi–LSTM, effectively enhancing the accuracy of upper limb motion intention perception and contributing to the improvement of rehabilitation training effects.

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IROS Conference 2025 Conference Paper

Micro-robotic Swarm of Silicone Oil-based Ferrofluid's Microdroplets

Yulei Fu
Hengao Yu
Leilei Chen
Zhiteng Zheng
Wendong Wang

Microscale droplet-based robotic systems have emerged as a promising platform for targeted drug delivery, minimally invasive surgery, and lab-on-a-chip applications. Here, we report a novel microrobotic swarm based on microdroplets of silicone oil-based ferrofluid, which exhibits excellent biocompatibility and chemical inertness. By modulating three-dimensional magnetic fields, we achieved reconfigurable self-organized patterns of an aggregated state, a dispersed state, and a chain state. We established a dynamic model and reproduced the three states via numerical simulations. Furthermore, we discovered two locomotion modes: sliding and rolling. Utilizing the sliding mode, we navigated the swarm through narrow and complex channels and accomplished directional transport of bubbles, enabling both translational and rotational movements.

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JBHI Journal 2022 Journal Article

A Scalable Graph-Based Framework for Multi-Organ Histology Image Classification

Yu Bai
Yue Mi
Yihan Su
Bo Zhang
Zheng Zhang
Jingyun Wu
Haiwen Huang
Yongping Xiong

Graph-based approaches are successful for histology image classification tasks but still face many challenges, such as: 1) the lack of nuclei-level labels and the significant variations between histology images make it extremely difficult to extract discriminative high-level nuclei features like nuclei type, texture and micro-environment; 2) graph-based approaches cannot handle large-scale cell graph nodes typically contained in histology images; and 3) graph neural networks (GNNs) struggle to learn the long-range dependency of cell graphs. To address the above challenges, we propose a scalable graph-based framework for multi-organ histology image classification. We develop a two-step masked nuclei patches supervised training approach to extract discriminative high-level nuclei features for histology images without nuclei-level labels. Additionally, we introduce a nuclei sampling strategy to make our graph-based framework scalable for large-scale cell graphs. Furthermore, we propose H ier A rchical T ransformer Graph Neural Net work (HAT-Net+) for cell graph classi- fications. HAT-Net+ adopts Transformer to model the long-range dependency of cell graphs and a parameter-free approach to adaptively fuse different hierarchical graph representations of each layer. We achieved the state-of-the-art results on four public histology image classification datasets: CRC dataset (100%), Extended CRC dataset (98%), UZH dataset (96. 9%) and BACH dataset (88%). Unlike other methods, our approach can be used in various histology image classification tasks, even for images without nuclei-level labels, indicating its potential in cancer diagnosis. The code is available at https://github.com/suyouooooo/HAT-Net.

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IROS Conference 2018 Conference Paper

Collectives of Spinning Mobile Microrobots for Navigation and Object Manipulation at the Air-Water Interface

Wendong Wang
Vimal Kishore
Lyndon Koens
Eric Lauga
Metin Sitti

We use multiple spinning micro-rafts at the air-water interface as mobile microrobot collectives and present here their collective behaviors, including navigating around anchored obstacles, and trapping and transporting floating objects. The 3D-printed micro-rafts are circular disKS of 100 μm in diameter and have parametrically defined undulating edge profile. The study of their local interactions, manifested by the pairwise interactions between micro-rafts, reveals competing magnetic and capillary interactions that keep the collectives in their dynamic state. Using collectives of 7, 19, and 36 micro-rafts and micro-channels between millimeter-sized posts, we demonstrate the effects of the size of the collectives, the size of the obstacles, and maneuver strategies on the collective navigation. Employing methods from information theory, we show that the pairwise mutual information of the collectives increases significantly during the channel-crossing as a result of the additional constraints of the channel walls on the collectives. Finally, we demonstrate the trapping of 1-mm-diameter polystyrene bead and the trapping and transporting of 600~μm-wide pm.

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TIST Journal 2015 Journal Article

An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints

Bo Zhang
Zheng Song
Chi Harold Liu
Jian Ma
Wendong Wang

Participatory sensing systems can be used for concurrent event monitoring applications, like noise levels, fire, and pollutant concentrations. However, they are facing new challenges as to how to accurately detect the exact boundaries of these events, and further, to select the most appropriate participants to collect the sensing data. On the one hand, participants’ handheld smart devices are constrained with different energy conditions and sensing capabilities, and they move around with uncontrollable mobility patterns in their daily life. On the other hand, these sensing tasks are within time-varying quality-of-information (QoI) requirements and budget to afford the users’ incentive expectations. Toward this end, this article proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints. The main method of this framework is event boundary detection. For the former, a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. Participants are selected by explicitly considering their mobility pattern, required QoI of multiple tasks, and users’ incentive requirements, under the constraint of an aggregated task budget. Extensive experimental results, based on a real trace in Beijing, show the effectiveness and robustness of our approach, while comparing with existing schemes.

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