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Bowen Liu

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13 papers

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JBHI Journal 2026 Journal Article

CoMIL: A Contrastive CNN-Transformer Framework with Multi-Instance Learning for Whole-Slide Pathology Image Classification

Bowen Liu
Hongbo Zhu
Xiaotong Wei
Chuan Lin
Wei Wang

Whole slide image (WSI) classification faces challenges due to gigapixel scale and weak supervision, often struggling to balance global context with local details. We propose CoMIL, a dual-branch framework based on symmetric mutual learning. Firstly, to resolve the dilemma where single-stream networks struggle to simultaneously capture global context and fine-grained details, we employ dual parallel pathways: a Transformer branch models long-range instance dependencies, while a CNN branch captures localized tissue morphology. Secondly, to address spatial information loss, we design a Hyper Positional Generator (HyperPG). This module integrates multi-scale adaptive mechanisms with deformable convolutions, enhancing spatial awareness with linear complexity. Finally, to improve model robustness against weak label noise, bidirectional learning between branches is achieved through KL divergence minimization. Extensive experiments show that our proposed method achieves an area under the curve of 98. 6% and an accuracy of 95. 3% on the Camelyon16 dataset, and an area under the curve of 98. 8% and an accuracy of 93. 3% on the TCGA_Kidney dataset, surpassing the performance of known advanced WSI classification methods.

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AAAI Conference 2026 Conference Paper

Cross-View Progressive Feature Filtering for Multi-View Graph Clustering in Remote Sensing

Bowen Liu
Xin Peng
Wenxuan Tu
Chengyao Wei
Xiangyan Tang
Jieren Cheng
Miao Yu

Multi-view clustering of remote sensing data plays a vital role in Earth observation analysis. Recently, deep graph clustering methods based on contrastive learning have significantly improved feature representation capabilities. However, most existing approaches treat all views equally, neglecting the inherent uniqueness and heterogeneity across views, which often results in two major issues: 1) discriminative features from clustering-friendly views are underexplored; and 2) redundant or noisy information from less informative views can degrade the shared representation. To address these challenges, we propose a novel multi-view graph clustering framework termed CF-MVGC for remote sensing data, which dynamically preserves discriminative features and suppresses redundancy by assessing view affinity. Specifically, we employ a dual-stage representation learning strategy to extract both view-specific discriminative features and cross-view consistent representations. To further exploit and adaptively integrate complementary information across views, we design a progressive feature filtering model that dynamically evaluates view affinity using two novel metrics, i.e., view fidelity index (VFI) and view criticality index (VCI). Based on these assessments, the module adaptively modulates feature update and reset signals, reinforcing informative views while suppressing noisy or redundant ones. Views with high affinity receive strengthened update signals to retain valuable features, while those with low affinity are subjected to enhanced reset operations to eliminate noise and redundancy. The resulting high-quality, discriminative representations lead to improved clustering performance, establishing a positive feedback loop. Experimental results on four benchmark datasets demonstrate the effectiveness and superiority of CF-MVGC against its competitors.

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JBHI Journal 2026 Journal Article

Synthesis Image Editing for Attribute Evolution in the Pseudo-Temporal Sequence of Pulmonary Nodule Growth

Hongbo Zhu
Bowen Liu
Xiaotong Wei
Guangjie Han
Wenbo Zhang
Yue Ma
Aso Darwesh

Medical Mixed Reality (MR) has made significant progress in virtual surgery simulation and tumor teaching. This paper proposes a framework for pulmonary nodule attribute editing based on image feature consistency, achieving spatial alignment of multi-stage case data. To address the limitations of traditional time-image reconstruction, we design an adversarial siamese model architecture capable of synthesizing missing nodule images, completing temporal data, and fine-grained modeling of nodule growth. To tackle challenges such as deformation, background inconsistency, and attribute uncertainty in generated samples, we introduce a Denoising Diffusion Implicit Model (DDIM) and construct an attribute vector space for pathological feature editing. Additionally, we propose a separable image reconstruction strategy to enhance local feature stability. Extensive validation on the lung-specific LIDC-IDRI dataset demonstrates superior performance with SSIM of 97. 5 ${\%}$ and LPIPS of 0. 036. To further verify generalization capability, cross-organ testing on the liver-focused LiTS dataset achieves competitive results with SSIM of 85. 0 ${\%}$ and LPIPS of 0. 128. These outcomes provide strong technical support for high-fidelity virtual surgery and intelligent tumor teaching platforms.

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JBHI Journal 2026 Journal Article

ViG3D-UNet: Volumetric Vascular Connectivity-Aware Segmentation via 3D Vision Graph Representation

Bowen Liu
Chunlei Meng
Wei Lin
Hongda Zhang
Ziqing Zhou
Zhongxue Gan
Chun Ouyang

Accurate vascular segmentation is essential for coronary visualization and the diagnosis of coronary heart disease. This task involves the extraction of sparse tree-like vascular branches from volumetric space. However, existing methods have faced significant challenges due to discontinuous vascular segmentation and missing endpoints. To address this issue, a 3D vision graph neural network framework, named ViG3D-UNet, was introduced. This method integrates 3D graph representation and aggregation within a U-shaped architecture to facilitate continuous vascular segmentation. The ViG3D module captures volumetric vascular connectivity and topology, while the convolutional module extracts fine vascular details. These two branches are combined through channel attention to form the encoder feature. Subsequently, a paperclip-shaped offset decoder minimizes redundant computations in the sparse feature space and restores the feature map size to match the original input dimensions. To evaluate the effectiveness of the proposed approach for continuous vascular segmentation, evaluations were performed on two public datasets, ASOCA and ImageCAS. The segmentation results show that the ViG3D-UNet surpassed competing methods in maintaining vascular segmentation connectivity while achieving high segmentation accuracy.

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TAAS Journal 2025 Journal Article

A Consortium Blockchain-Based Edge Task Offloading Method for Connected Autonomous Vehicles

Bowen Liu
Hao Tian
Zhijie Shen
Yueyue Xu
Wanchun Dou

In recent years, the proliferation of Connected Autonomous Vehicles (CAV) has revolutionized the transportation industry. However, these vehicles often face limitations in terms of local computing resources, leading to the need for offloading interactive-intensive application tasks to servers for processing. Traditional paradigm has its limitations in meeting the demands of massive task processing. The combination of Web3.0 and edge computing offers users high-reliable, low-latency, and highly flexible services. Nevertheless, the new paradigm also presents its own challenges such as ensuring privacy data protection, and reducing the time and energy costs associated with task offloading. To tackle these challenges, an edge task offloading framework based on consortium blockchain for CAVs has been developed. Within this framework, a consortium blockchain-based interaction-intensive task offloading method, called CBIToMe, has been designed. CBIToMe specifically addresses the multi-stage nature of interactive-intensive CAV tasks and aims to minimize task completion time and offloading costs, particularly when the waiting time for interaction is uncertain. Additionally, CBIToMe effectively utilizes consortium blockchain technology to safeguard the CAV privacy data. Results from experiments conducted in various scenarios demonstrate that CBIToMe outperforms three representative methods, showcasing its superior performance.

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JBHI Journal 2025 Journal Article

Fine-Grained Classification Reveals Angiopathological Heterogeneity of Port Wine Stains Using OCT and OCTA Features

Xiaofeng Deng
Defu Chen
Bowen Liu
Xiwan Zhang
Haixia Qiu
Wu Yuan
Hongliang Ren

Accurate classification of port wine stains (PWS, vascular malformations present at birth), is critical for subsequent treatment planning. However, the current method of classifying PWS based on the external skin appearance rarely reflects the underlying angiopathological heterogeneity of PWS lesions, resulting in inconsistent outcomes with the common vascular-targeted photodynamic therapy (V-PDT) treatments. Conversely, optical coherence tomography angiography (OCTA) is an ideal tool for visualizing the vascular malformations of PWS. Previous studies have shown no significant correlation between OCTA quantitative metrics and the PWS subtypes determined by the current classification approach. In this study, we propose a novel fine-grained classification method for PWS that integrates OCT and OCTA imaging. Utilizing a machine learning-based approach, we subdivided PWS into five distinct subtypes by unearthing the heterogeneity of hypodermic histopathology and vessel structures. Six quantitative metrics, encompassing vascular morphology and depth information of PWS lesions, were designed and statistically analyzed to evaluate angiopathological differences among the subtypes. Our classification reveals significant distinctions across all metrics compared to conventional skin appearance-based subtypes, demonstrating its ability to accurately capture angiopathological heterogeneity. This research marks the first attempt to classify PWS based on angiopathology, potentially guiding more effective subtyping and treatment strategies for PWS.

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YNIMG Journal 2025 Journal Article

Neuroplastic differentiation in motor cortex subregions induced by basketball training: A multimodal diffusion MRI investigation

Wenshuang Tang
Yihan Wang
Yapeng Qi
Wenxuan Fang
Xinwei Li
Bowen Liu
Jilan Ning
Jiaxin Du

The primary motor cortex (M1) contains two functionally distinct subregions: effector subregions, responsible for fine motor control, and inter-effector subregions, involved in mind-body coordination and movement planning. However, the impact of long-term exercise training on subregion-specific microstructural plasticity in M1 remains unclear. In this study, thirty-four elite basketball athletes and thirty-five age- and gender-matched non-athletes were included in the analysis. All participants underwent T1-weighted imaging and diffusion MRI scanning. Probabilistic fiber tracking was employed to delineate distinct subregions within the M1. Diffusion MRI techniques, including diffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging, were employed to assess microstructural differences. The athletes' cognitive-motor integration performance was assessed by the swimmy paradigm. Compared to non-athletes, our results indicate that basketball athletes exhibited significantly decreased mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), orientation dispersion index (ODI), and free water fraction (FWF), and significantly increased axial kurtosis (AK) and neurite density index (NDI) in the inter-effector subregions of M1. No significant differences were observed in the effector-specific subregions. Correlation analyses revealed that the difference of reaction times was negatively correlated with MD\AD\RD and FWF, and positively correlated with NDI in the inter-effector subregions. These findings suggest that basketball training induces region-specific microstructural changes in M1, primarily in the inter-effector subregions, which are closely linked to cognitive-motor integration performance. The neuroplastic mechanisms induced by basketball training, as revealed in elite athletes, provide a rationale for exploring sport-based neuromodulatory interventions to optimize cognitive-motor rehabilitation.

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AAAI Conference 2025 Conference Paper

PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar Classifiers

Siyuan Wu
Yongzhe Jia
Bowen Liu
Haolong Xiang
Xiaolong Xu
Wanchun Dou

Personalized federated learning (PFL) has recently gained significant attention for its capability to address the poor convergence performance on highly heterogeneous data and the lack of personalized solutions of traditional federated learning (FL). Existing mainstream approaches either perform personalized aggregation based on a specific model architecture to leverage global knowledge or achieve personalization by exploiting client similarities. However, the former overlooks the discrepancies in client data distributions by indiscriminately aggregating all clients, while the latter lacks fine-grained collaboration of classifiers relevant to local tasks. In view of this challenge, we propose a Personalized Federated learning method for Enhancing Collaboration among Similar Classifiers (PFedCS), which aims at improving the client’s accuracy on local tasks. Concretely, it is achieved by leveraging awareness of the client classifier similarities to address the above problems. By iteratively measuring the distance of the classifier parameters between clients and clustering with each client as a cluster center, the central server adaptively identifies the collaborating clients with similar data distributions. In addition, a distance-constrained aggregation method is designed to generate customized collaborative classifiers to guide local training. As a result, extensive experimental evaluations conducted on three datasets demonstrate that our method achieves state-of-the-art performance.

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JBHI Journal 2025 Journal Article

RTS-ViT: Real-Time Share Vision Transformer for Image Classification

Chunlei Meng
Wei Lin
Bowen Liu
Hongda Zhang
Zhongxue Gan
Chun Ouyang

Vision transformers have achieved remarkable success in image classification. The dual-branch vision transformer generates more features by taking advantage of feature fusion. Inspired by this, a dual-branch vision transformer with Real-Time Share feature was proposed during the encoding process for retinal image classification tasks. The approach processes image patches of varying sizes (base and large) through two independent branches and implements multi-stage Real-Time feature fusion via the Real-Time Share feature encoder. This encoder enables the branches to complement each other's features at each encoding stage, facilitating finer feature learning and enhancing the self-attention information passed to subsequent stages. It significantly boosts feature representation and classification performance. Additionally, a straightforward and effective feature fusion method, L -Times Attention Fusion, was proposed: vector concatenation for Real-Time Share feature in the earlier ( L -1) encoding stages and element-wise addition for overall feature fusion at the L -th stage, achieving more efficient feature integration. The method was validated on a retinal image dataset. Results show that the approach outperforms the recent Cross-ViT average TOP-1 Acc by 5. 61% with lower FLOPs and model parameters, without relying on pre-trained weights, highlighting stronger self-learning feature capabilities and reduced reliance on extensive pre-training data.

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AAAI Conference 2025 Conference Paper

Subgraph Aggregation for Out-of-Distribution Generalization on Graphs

Bowen Liu
Haoyang Li
Shuning Wang
Shuo Nie
Shanghang Zhang

Out-of-distribution (OOD) generalization in Graph Neural Networks (GNNs) has gained significant attention due to its critical importance in graph-based predictions in real-world scenarios. Existing methods primarily focus on extracting a single causal subgraph from the input graph to achieve generalizable predictions. However, relying on a single subgraph can lead to susceptibility to spurious correlations and is insufficient for learning invariant patterns behind graph data. Moreover, in many real-world applications, such as molecular property prediction, multiple critical subgraphs may influence the target label property. To address these challenges, we propose a novel framework, SubGraph Aggregation(SuGAr), designed to learn a diverse set of subgraphs that are crucial for OOD generalization on graphs. Specifically, SuGAr employs a tailored subgraph sampler and diversity regularizer to extract a diverse set of invariant subgraphs. These invariant subgraphs are then aggregated by averaging their representations, which enriches the subgraph signals and enhances coverage of the underlying causal structures, thereby improving OOD generalization. Extensive experiments on both synthetic and real-world datasets demonstrate that SuGAr outperforms state-of-the-art methods, achieving up to a 24% improvement in OOD generalization on graphs. To the best of our knowledge, this is the first work to study graph OOD generalization by learning multiple invariant subgraphs.

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JBHI Journal 2025 Journal Article

XFM: An Explainable Knowledge-Fused Vision Foundation Model for Improving Clinical Diagnosis of Low-Prevalence Retinal Diseases

Hongyang Jiang
Mengdi Gao
Bowen Xu
Bowen Liu
Peilun Shi
Yibing Wang
Dajun Liu
Wu Yuan

Foundation models in ophthalmology, often pre-trained on extensive datasets, exhibit exceptional generalization and emergent capabilities that are absent in smaller-scale specialized models. This study first investigated the adaptation of ophthalmic foundation models to detect low-prevalence retinal diseases in real-world clinical settings with low-data regimes. We then bridged the gap in exploring the use of fine-grained prior-knowledge infusion and SAM-guided cycle constraint regularization to enhance the explainability of the foundation models from both qualitative and quantitative perspectives. Evaluated on two newly constructed public datasets (FundusData-FS and OTFID), our foundation model-based solution demonstrates effective transfer learning and few-shot learning fine-tuning performance for multiple low-prevalence retinal diseases. Our experiments demonstrate that prior-knowledge infusion and SAM-guided regularization enhance both the performance and the explainability of the foundation model. For example, our method achieves over 9% accuracy improvement and superior AUC performance (an 8% gain over RETFound) in GradCAM-positive perturbation testing, with statistically significant improvements (p <0. 05) in the FundusData-FS dataset. These findings highlight the potential of explainable ophthalmic foundation models for trustworthy AI in clinical practice.

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NeurIPS Conference 2020 Conference Paper

Open Graph Benchmark: Datasets for Machine Learning on Graphs

Weihua Hu
Matthias Fey
Marinka Zitnik
Yuxiao Dong
Hongyu Ren
Bowen Liu
Michele Catasta
Jure Leskovec

We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at https: //ogb. stanford. edu.

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NeurIPS Conference 2018 Conference Paper

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

Jiaxuan You
Bowen Liu
Zhitao Ying
Vijay Pande
Jure Leskovec

Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as drug-likeness and synthetic accessibility, while obeying physical laws such as chemical valency. However, designing models that finds molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task. Here we propose Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goal-directed graph generation through reinforcement learning. The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient, and acts in an environment that incorporates domain-specific rules. Experimental results show that GCPN can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improvement on the constrained property optimization task.

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