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Zhendong Chen

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4 papers

2 author rows

JBHI Journal 2026 Journal Article

HCA-Net: Hierarchical Contextual Attention Network for Lightweight and Accurate Polyp Segmentation

Chengcheng Li
Huiying Xu
Xinzhong Zhu
Huiling Chen
Xinwang Liu
Yun Liu
Chang Tang
Zhendong Chen

Early detection of colorectal polyps is crucial for clinical screening and cancer prevention, where accurate and efficient automatic segmentation plays a pivotal role. However, colonoscopy images often suffer from low contrast, blurred boundaries, and scale variations, making segmentation challenging. Existing encoder-decoder networks (e. g. , U-Net) suffer from asymmetric supervision and feature redundancy, which in turn lead to semantic inconsistency and loss of fine details. While deeper or hybrid designs alleviate these issues, their high complexity and computational burden limit feasibility in real-time clinical practice. To address these challenges, we propose a lightweight segmentation framework, Hierarchical Contextual Attention Network (HCA-Net), consisting of the Redundancy-Suppressed Dual-Path Downsampling (RS-DPD) module and the Boundary-Aware Semantic Alignment Upsampling (BA-SAU) module, applied to the encoder and decoder, respectively. RS-DPD suppresses redundancy while preserving fine-grained details through a dual-path design, whereas BA-SAU leverages cross-layer contextual attention to enforce semantic consistency and enhance boundary sensitivity. Both modules are built upon our proposed Hierarchical Contextual Attention (HCA) mechanism, which combines convolutional projection with pooling-based compression to achieve efficient global modeling and accurate local boundary restoration. In addition, a composite boundary-aware loss function is designed to improve pixel-level accuracy, structural consistency, and robustness in low-contrast and boundary-ambiguous regions. Extensive experiments on public colorectal polyp datasets demonstrate that HCA-Net achieves state-of-the-art (SOTA) segmentation accuracy with significantly improved efficiency, while maintaining robustness under low-contrast and blurred-boundary conditions.

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IROS Conference 2025 Conference Paper

Automatic MILP Model Construction for Multi-Robot Task Allocation and Scheduling Based on Large Language Models

Mingming Peng
Zhendong Chen
Jie Yang
Jin Huang
Zhengqi Shi
Qihao Liu
Xinyu Li 0001
Liang Gao 0001

With the accelerated development of Industry 4. 0, intelligent manufacturing systems increasingly require efficient task allocation and scheduling in multi-robot systems. However, existing methods rely on domain expertise and face challenges in adapting to dynamic production constraints. Additionally, enterprises have high privacy requirements for production scheduling data, which prevents the use of cloud-based large language models (LLMs) for solution development. To address these challenges, there is an urgent need for an automated modeling solution that meets data privacy requirements. This study proposes a knowledge-augmented mixed integer linear programming (MILP) automated formulation framework, integrating local LLMs with domain-specific knowledge bases to generate executable code from natural language descriptions automatically. The framework employs a knowledge-guided DeepSeek-R1-Distill-Qwen-32B model to extract complex spatiotemporal constraints (82% average accuracy) and leverages a supervised fine-tuned Qwen2. 5-Coder-7B-Instruct model for efficient MILP code generation (90% average accuracy). Experimental results demonstrate that the framework successfully achieves automatic modeling in the aircraft skin manufacturing case while ensuring data privacy and computational efficiency. This research provides a low-barrier and highly reliable technical path for modeling in complex industrial scenarios.

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IS Journal 2023 Journal Article

Effectively Modeling Sentence Interactions With Factorization Machines for Fact Verification

Zhendong Chen
Fuzhen Zhuang
Lejian Liao
Meihuizi Jia
Jiaqi Li
Heyan Huang

Fact verification is a very challenging task that requires retrieving multiple evidence sentences from a reliable corpus to authenticate claims. Many claims require the simultaneous integration and reasoning of several pieces of evidence for verification. Existing models exhibit limitations in two aspects: 1) during the sentence selection stage, they only consider the interaction between the claim and the evidence, disregarding the intersentence information, and 2) most fusion strategies employed in current research, such as addition, concatenation, or simple neural networks, fail to capture the relationships and logical information among the evidence. To alleviate these problems, we propose select and fact verification modeling (SFVM). Our model utilizes a multihead self-attention mechanism combined with a gating mechanism to facilitate sentence interaction and enhance sentence embeddings. Then, we utilize factorization machines to effectively express the compressed alignment vectors, which are then used to expand the representations of the base evidence. To distinguish the importance of features, we use the evidence fusion network to determine the importance of various feature interactions. Results from experiments on the two public datasets showed that SFVM can utilize richer information between the claim and the evidence for fact verification and achieve competitive performance on the FEVER dataset.

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AAAI Conference 2023 Conference Paper

MNER-QG: An End-to-End MRC Framework for Multimodal Named Entity Recognition with Query Grounding

Meihuizi Jia
Lei Shen
Xin Shen
Lejian Liao
Meng Chen
Xiaodong He
Zhendong Chen
Jiaqi Li

Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods either (1) obtain named entities with coarse-grained visual clues from attention mechanisms, or (2) first detect fine-grained visual regions with toolkits and then recognize named entities. However, they suffer from improper alignment between entity types and visual regions or error propagation in the two-stage manner, which finally imports irrelevant visual information into texts. In this paper, we propose a novel end-to-end framework named MNER-QG that can simultaneously perform MRC-based multimodal named entity recognition and query grounding. Specifically, with the assistance of queries, MNER-QG can provide prior knowledge of entity types and visual regions, and further enhance representations of both text and image. To conduct the query grounding task, we provide manual annotations and weak supervisions that are obtained via training a highly flexible visual grounding model with transfer learning. We conduct extensive experiments on two public MNER datasets, Twitter2015 and Twitter2017. Experimental results show that MNER-QG outperforms the current state-of-the-art models on the MNER task, and also improves the query grounding performance.

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