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Hongwei Ge

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9 papers

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JBHI Journal 2026 Journal Article

Tumor Contraction-Aware Multi-Sequence MRI Framework for Accurate Post-Ablation Margin Assessment in Hepatocellular Carcinoma

Linan Dong
Hongwei Ge
Jie Yu
Yong Luo
Jinming Hu
Shichen Yu
Ping Liang

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality, and microwave ablation (MWA) is commonly used for patients ineligible for surgical resection. A critical challenge following MWA is the assessment of the ablative margin, which is complicated by non-diffeomorphic deformations introduced by thermal effects during the procedure. This paper proposes a Multi-sequence Distance-guided Complementary Network (MDCNet) that utilizes multi-sequence MRI to quantify the extent of tumor contraction after MWA. To account for the differential contraction responses of liver parenchyma and tumor tissue, we propose a novel distance-aware mask transformation strategy. This method explicitly models the spatial attenuation of MWA energy and approximates the influence of liver parenchyma's linear elastic response on tumor shrinkage, thereby enhancing the spatial adaptiveness of feature weighting. To capture the distinct structural characteristics of liver tissue emphasized by different MRI sequences and to leverage their complementary information, a gated channel fusion module is introduced to dynamically integrate features from delayed-phase and T2-weighted images. To validate the practical effectiveness of our proposed method, we evaluate the ablative margins of 115 HCC patients using a fine-tuned TransMorph model that incorporated tumor contraction predictions generated by MDCNet, and compare the results with radiologist 2D assessments. The registration method enhanced with MDCNet improved tumor deformation accuracy and achieved a higher Youden Index in detecting incomplete ablations. Moreover, MDCNet provides interpretable predictions, thereby facilitating clinical decision support.

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AIIM Journal 2025 Journal Article

A prior knowledge-supervised fusion network predicts survival after radiotherapy in patients with advanced gastric cancer

Liang Sun
Yongxin Lan
Jian Sun
Pengfei Ji
Hongwei Ge
Ming Cui
Xin Yuan

BACKGROUND AND OBJECTIVE: Predicting overall survival (OS) for advanced gastric cancer patients after radiotherapy is critical for developing an individualized treatment plan. However, existing studies have focused on gastric cancer CT images with a large amount of redundant information, neglecting the role of physicians' prior knowledge in guiding gastric cancer CT image information. We propose a multimodal fusion method based on prior knowledge to predict OS after radiotherapy in advanced gastric cancer patients to assist physicians in clinical diagnosis and treatment. METHODS: A prior knowledge supervised fusion network (PKSFnet) is proposed. Firstly, PKSFnet uses a novel sampling strategy, which enables the input model data to obtain a complete feature space by analyzing the entire patient data space. Afterwards, under the guidance of the multi-domain feature fusion module (MdFF), multimodal information of patients is adaptively fused and mined to improve the prediction performance. RESULTS: The results of the proposed model are superior to those of other unimodal and multimodal state-of-the-art methods. For the segmented survival time classification task, the AUC, specificity, sensitivity, precision of the proposed model are 0.8397, 0.875, 0.7556, and 0.875, respectively. For the survival risk regression task, the C-index and HR of the proposed model are 0.8574 and 4.658 respectively. Ablation experimental results further demonstrate the impact of each module of the proposed model. Finally, we apply the novel sampling strategy to other deep learning models and achieve significant improvement. CONCLUSION: The experimental results have demonstrated that the proposed model can effectively predict OS after radiotherapy in patients with advanced gastric cancer, which demonstrate that the proposed model can facilitate the development and application of robust clinical treatment strategies.

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EAAI Journal 2025 Journal Article

An evolutionary multitasking algorithm for multi-objective feature selection using dual-perspective reduction

Mengyue Wang
Hongwei Ge
Xia Wang
Liang Sun
Yaqing Hou
Bin Li

Feature selection inherently involves two conflicting objectives: minimizing the number of selected features and maximizing the classification accuracy. The exponential growth of the search space and complex interactions between features make high-dimensional feature selection challenging. Existing multi-objective methods suffer from slow convergence and limited search capabilities. Moreover, there is a lack of efficient methods for identifying feature subsets with equivalent objective values, which could offer diverse options. To address these issues, this paper proposes an evolutionary multitasking algorithm for multi-objective feature selection using dual-perspective reduction, called DREA-FS. First, a dual-perspective dimensionality reduction strategy is designed to generate simplified and complementary tasks through improved filter-based and group-based methods, facilitating the rapid identification of promising regions. To enable effective information sharing, a dual-archive multitasking optimization mechanism is proposed, which incorporates a diversity archive to preserve feature subsets with equivalent performance and maintain diversity. Coupled with an elite archive that offers convergence guidance, this mechanism achieves a balance between convergence and diversity across tasks, thereby enhancing the ability to search for equivalent feature subsets. Experimental results on 21 datasets demonstrate that the proposed method outperforms state-of-the-art multi-objective algorithms in classification performance. Besides, DREA-FS can identify different feature subsets with equivalent objective values, supporting decision-makers with diverse options and better interpretability.

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IJCAI Conference 2025 Conference Paper

EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems

Dian Meng
Zhiguang Cao
Yaoxin Wu
Yaqing Hou
Hongwei Ge
Qiang Zhang

Recent neural heuristics for the Vehicle Routing Problem (VRP) primarily rely on node coordinates as input, which may be less effective in practical scenarios where real cost metrics—such as edge-based distances—are more relevant. To address this limitation, we introduce EFormer, an Edge-based Transformer model that uses edge as the sole input for VRPs. Our approach employs a precoder module with a mixed-score attention mechanism to convert edge information into temporary node embeddings. We also present a parallel encoding strategy characterized by a graph encoder and a node encoder, each responsible for processing graph and node embeddings in distinct feature spaces, respectively. This design yields a more comprehensive representation of the global relationships among edges. In the decoding phase, parallel context embedding and multi-query integration are used to compute separate attention mechanisms over the two encoded embeddings, facilitating efficient path construction. We train EFormer using reinforcement learning in an autoregressive manner. Extensive experiments on the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) reveal that EFormer outperforms established baselines on synthetic datasets, including large-scale and diverse distributions. Moreover, EFormer demonstrates strong generalization on real-world instances from TSPLib and CVRPLib. These findings confirm the effectiveness of EFormer’s core design in solving VRPs.

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EAAI Journal 2025 Journal Article

Hypergraph-driven soft semantics flexible learning for visible–infrared person re-identification

Jiacheng Zhu
Hongwei Ge
Yuxuan Liu
Chunguo Wu
Jiulin Fan

Visible–infrared person re-identification (VI-ReID) aims to match the images of the person across different modalities. The main challenge in the engineering application of VI-ReID lies in the considerable modality gap between visible and infrared images. Existing methods primarily focus on learning low-order hard semantics to reduce the modality gap, such as specific body parts (e. g. , arms, legs, and hands), which are modality-specific and sensitive to cross-modality variations, leading to difficult modality alignment. However, high-order soft semantics contain more modality-invariant features that can be flexibly learned by the model to better align the cross-modality features and reduce the modality gap. To better learn soft semantics, we propose a novel, hypergraph-driven soft semantics flexible learning network (HSFLNet), which extracts hierarchical semantics and flexibly explores the relationships among the extracted soft semantics by using hypergraph neural networks (HGNNs). Specifically, first, we propose a soft semantics mining (SSM) module to capture and flexibly fuse hierarchical features, which integrates low- and high-level semantics across channel and spatial dimensions to align modalities. Second, a hypergraph-driven soft semantics flexible learning (HSFL) module is designed, which employs HGNNs to explore the relationships among soft semantics. These semantics are adaptively learned through gating mechanisms to capture modality-invariant features, thereby reducing the modality gap. We evaluate the performance of HSFLNet on SYSU-MM01, RegDB, and low-light cross-modality (LLCM) datasets, achieving a Rank-1/mean average precision of 75. 09%/72. 11%, 95. 82%/92. 42%, and 65. 42%/68. 55%, respectively. Experimental results demonstrate that HSFLNet outperforms state-of-the-art VI-ReID methods. Our code is available at https: //github. com/Jiacheng813/HSFLNet.

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IJCAI Conference 2024 Conference Paper

SAEIR: Sequentially Accumulated Entropy Intrinsic Reward for Cooperative Multi-Agent Reinforcement Learning with Sparse Reward

Xin He
Hongwei Ge
Yaqing Hou
Jincheng Yu

Multi-agent reinforcement learning (MARL) performs well for solving complex cooperative tasks when the scenarios have well-defined dense rewards. However, there are usually sparse reward settings in many real-world multi-agent systems, which makes it difficult for MARL algorithms to successfully learn an effective strategy. To tackle this problem, we propose a novel sequentially accumulated entropy intrinsic reward named SAEIR, which utilizes the entropy of multi-agent system as a bonus to accelerate learning. Specifically, the multi-scale hypergraph critic is proposed to obtain high-order system state representation, which also enhances the ability to effectively evaluate the action produced by the actor. Based on the comprehensive and compact system state representation, the orderliness of multi-agent systems can be measured to determine the highly valuable states for adding entropy-based intrinsic rewards which leads to a highly efficient learning process. Empirical results demonstrate that our proposed method achieves state-of-the-art performance in several complex cooperative multi-agent environments with sparse reward settings.

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AAMAS Conference 2023 Conference Paper

Improving Cooperative Multi-Agent Exploration via Surprise Minimization and Social Influence Maximization

Mingyang Sun
Yaqing Hou
Jie Kang
Haiyin Piao
Yifeng Zeng
Hongwei Ge
Qiang Zhang

In multi-agent reinforcement learning (MARL), the uncertainty of state change and the inconsistency between agents’ local observation and global information are always the main obstacles of cooperative multi-agent exploration. To address these challenges, we propose a novel MARL exploration method by combining surprise minimization and social influence maximization. Considering state entropy as a measure of surprise, surprise minimization is achieved by rewarding the individual’s intrinsic motivation (or rewards) for coping with more stable and familiar situations, hence promoting the policy learning. Furthermore, we introduce mutual information between agents’ actions as a regularizer to maximize the social influence via optimizing a tractable variational estimation. In this way, the agents are guided to interact positively with one another by navigating between states that favor cooperation.

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EAAI Journal 2023 Journal Article

Research on an unsupervised person re-identification based on image quality enhancement method

Zhangang Hao
Hongwei Ge
Jiajian Huang

Research on person re-identification(Re-ID) has important value in pedestrian detection, target tracking, criminal investigation, and other related fields. In unsupervised pedestrian recognition algorithms, the accuracy of pseudo-labels is crucial to the recognition results. However, in practical scenarios, low-quality images caused by factors such as differences in camera resolution and shooting angles can affect the extraction of pedestrian features by these algorithms, thereby negatively impacting the accuracy of the labels and the learning process of the model. To address this problem, we propose an image quality enhancement algorithm for unsupervised person Re-ID (IQE). To the best of our knowledge, this study is the first to introduce detail enhancement and the application of low-light enhancement algorithms into unsupervised person Re-ID. By improving the feature extraction quality based on these two aspects, higher-quality pseudo-labels can be constructed. This method improves the accuracy of feature extraction and clustering, thereby increasing the accuracy of pseudo-labels and reducing the interference of noisy pseudo-labels. The experimental results showed that the IQE method outperformed state-of-the-art person Re-ID methods in terms of Rank-1 accuracy and mAP. Specifically, IQE achieved an 87. 9% rank-1 accuracy and a 71. 2% mAP on the Market-1501 dataset; a 78. 1% rank-1 accuracy and a 61. 7% mAP On the DukeMTMC-reID dataset; and a 51. 1% rank-1 accuracy and 24. 2% mAP on the MSMT17 dataset.

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EAAI Journal 2023 Journal Article

Robust multi-view subspace enhanced representation based on collaborative constraints and HSIC induction

Guoqing Liu
Hongwei Ge
Ting Li
Shuzhi Su
Shuangxi Wang

The design of effective multi-view subspace clustering (MSC) algorithms has recently garnered significant research attention. Herein, to effectively improve the recognition performance and anti-noise interference ability of an MSC model, we propose a novel MSC algorithm, termed as robust multi-view subspace enhancement representation, based on collaborative constraints and a Hilbert–Schmidt independence criterion (HSIC) induction method. To mine the complementary information between different views, we apply the HSIC as a diversity regularization term. Specifically, to enhance the diagonal block structure of a subspace representation, a new sparse constraint is introduced on the product of itself and the transpose of the subspace representation matrix in a multi-view subspace learning model. Furthermore, hypergraph regularization and a low-rank idea are considered to capture the local geometric structure and clean data. In addition, to optimize our model, we adopt an augmented Lagrangian multiplier method and discuss the convergence of the model. Extensive experiments on six challenging datasets reveal that the proposed method achieves a highly competent objective performance with and without noisy views, as compared with several state-of-the-art multi-view clustering methods.

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