Author name cluster

Shuang Chen

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.

10 papers

1 author row

AIIM Journal 2026 Journal Article

Automation or augmentation? The impact of artificial intelligence's technological characteristics on usage intention in medical staff

Xiqian Zou
Shuang Chen

Artificial intelligence (AI) is revolutionizing clinical practice. As medical AI becomes increasingly a part of regular practice and procedures, a deeper understanding of the AI usage intention of medical staff is of great value. By incorporating technology-task fit and self-determination theory, this study developed a conceptual model through which to identify and observe the effects of medical AI automation and augmentation on the psychological needs (i. e. , autonomy, relatedness, and competence), technology-task fit, and AI usage intention of medical staff. Using cross-sectional data from 400 Chinese medical staff, a partial least squares structural equation model (PLS-SEM) analysis showed that medical AI automation correlated positively with perceived autonomy and competence, while augmentation correlated positively with perceived autonomy, competence, and relatedness. Mediation analysis indicated that perceived autonomy, competence, and technology-task fit were sequential mediators in the association between medical AI automation and usage intention; perceived autonomy, competence, relatedness, and technology-task fit were sequential mediators in the link between medical AI augmentation and usage intention. These findings exemplify the automation–augmentation paradox in AI research, offering insight into the AI usage intention of medical staff and the underlying psychological mechanisms involved. Strategies to facilitate medical staff's clinical AI usage intention stemming from a human-centered perspective are also proposed.

Details DOI

AAAI Conference 2026 Conference Paper

Exploring the Potentials of Spiking Neural Networks for Image Deraining

Shuang Chen
Tom Krajnik
Farshad Arvin
Amir Atapour-Abarghouei

Biologically plausible and energy-efficient frameworks such as Spiking Neural Networks (SNNs) have not been sufficiently explored in low-level vision tasks. Taking image deraining as an example, this study addresses the representation of the inherent high-pass characteristics of spiking neurons, specifically in image deraining and innovatively proposes the Visual LIF (VLIF) neuron, overcoming the obstacle of lacking spatial contextual understanding present in traditional spiking neurons. To tackle the limitation of frequency-domain saturation inherent in conventional spiking neurons, we leverage the proposed VLIF to introduce the Spiking Decomposition and Enhancement Module and the lightweight Spiking Multi-scale Unit for hierarchical multi-scale representation learning. Extensive experiments across five benchmark deraining datasets demonstrate that our approach significantly outperforms state-of-the-art SNN-based deraining methods, achieving this superior performance with only 13% of their energy consumption. These findings establish a solid foundation for deploying SNNs in high-performance, energy-efficient low-level vision tasks.

PDF Details DOI

EAAI Journal 2026 Journal Article

MAFSA: A multi-layer asynchronous federated learning with staleness-awareness in edge computing

Shiwen Zhang
Shuang Chen
Yujia Zhang
Wei Liang
Kuanching Li
Ling-Huey Li
Keqin Li

Federated Learning (FL) has emerged as a promising privacy-preserving scheme in edge computing. However, traditional cloud-based FL architectures still suffer from high communication overhead, which motivates the development of hierarchical and asynchronous variants to improve communication efficiency. However, traditional cloud-side-end architecture must wait for the results of all devices to complete the update, resulting in inefficient training. Furthermore, the phenomenon of end-device dropouts can lead to the waste of resources, thereby compromising the system’s fairness. In this work, we propose MAFSA, a multi-layer asynchronous federated learning scheme with staleness-awareness, which aims to enhance the system’s efficiency and resource utilization while maintaining accuracy and ensuring fairness. MAFSA proposes a composite asynchronous aggregation strategy to address the inefficiency issue caused by device heterogeneity and enhance the system’s communication efficiency. In addition, MAFSA proposes a staleness-awareness mechanism to cope with end device dropping, improve resource utilization, and ensure the system’s fairness. Extensive experiments demonstrate that the proposed scheme effectively utilizes stale information, significantly benefiting the federated learning system and proving the method’s effectiveness.

Details DOI

JBHI Journal 2025 Journal Article

DAM: Degradation-Aware Model for Ultrasound Image Quality Assessment

Tuo Liu
Xuan Zhang
Xiuzhu Ma
Shuang Chen
Xuejuan Wang
Ping Zhou
Yang Chen
Guangquan Zhou

One of the core challenges in ultrasound image quality assessment (IQA) is the entanglement of semantic content and quality-related information, such as blurring and shadows. Insufficient attention to the latter can easily lead to biased IQA results. Furthermore, fine-grained quality inconsistencies, i. e. , subtle variations in ultrasound images that can impact quality interpretations, may further complicate the IQA tasks. To address these challenges, we propose a novel degradation-aware model (DAM) for the ultrasound IQA, which effectively perceives various and subtle variations of quality patterns, accurately assessing the quality of ultrasound images. The advanced degradation-derived augmentation (DDA) in DAM incorporates degradations that clinicians may focus on during IQA into the synthesis of appearance changes, promoting the disentanglement of quality-related representations from semantic contents. Subsequently, we present fine-grained degradation learning (FGDL), which encourages distinctions between image versions with diminishing quality inconsistencies, boosting the awareness of quality nuances from easy to hard for better ultrasound IQA performance. A universal boundary acquisition operator (UBAO) is also developed to suppress interferences from redundant information, achieving the standardization of ultrasound images from various devices. Extensive experimental results on an in-house ultrasound dataset demonstrate that DAM outperforms 14 baseline methods, achieving a PLCC of 0. 760 and an SROCC of 0. 766. The code can be available at this URL.

Details DOI

EAAI Journal 2025 Journal Article

Key node propagation-based overlapping spammer group detection algorithm on e-commerce platforms

Chaoqun Wang
Ning Li
Shuang Chen
Xiaoqing Bu
Shujuan Ji

With the rapid growth of e-commerce platforms, spammer groups have increasingly used fake reviews to influence consumer decisions, posing significant challenges to platform governance. This issue has become even more pronounced with the widespread use of large language models, which have made fake reviews harder to detect. However, existing spammer group detection algorithms have certain limitations. For example, they often overlook the core–periphery structure within spammer groups, failing to adequately focus on the core reviewers who play a crucial role in group operations. Additionally, these algorithms struggle to detect spammers who are active across multiple groups. To address these challenges, we propose an overlapping spammer group detection algorithm based on key node propagation (KNP-OSG). First, we model the review data as a co-review graph and use the Deep Q-Network algorithm combined with an action filtering mechanism to identify key reviewers, or key spammers, who have a critical impact on spammer group detection. Subsequently, based on the structural relationships among pivotal spammers, an improved label propagation algorithm, copra-g, is proposed to further identify spammer groups. Experimental results show that the KNP-OSG algorithm outperforms existing methods on real-world datasets, demonstrating its effectiveness in detecting overlapping spammer groups.

Details DOI

EAAI Journal 2025 Journal Article

Myocardial ischemic classification using a knowledge-guided polar transformer in two-dimensional echocardiography

Ziwei Pang
Yi Du
Yanhui Guo
Shuang Chen
Bo Yu
Siqi Guo
Guo-Qing Du

Myocardial ischemia, characterized by inadequate blood supply to the heart muscles, is critical to cardiovascular diseases. Timely and accurate identification of ischemic segments is essential for prompt intervention and patient care. This study developed a Transformer-based model to identify myocardial ischemia in left ventricle short-axis (LVSA) two-dimensional echocardiography (2DE) images where a novel Knowledge-Guided Polar Transformer (KGPT) was proposed that integrated the unique characteristics of 2DE images with the prior clinical knowledge. 305 patients (aged 57. 6 ± 8. 8 years) were selected and underwent transthoracic echocardiography within 1–3 days prior to invasive coronary angiography (ICA). With ICA and quantitative flow ratio as the gold standard of myocardial ischemia, the KGPT model was trained to classify the LVSA 2DE images as ischemia or non-ischemia by capturing spatial features in a radial orientation. Its performance was evaluated with five-fold cross-validation and receiver operating characteristic curve (ROC) analysis. It achieved an area under ROC (AUC) of 0. 8326 ± 0. 0906, with an accuracy of 79. 50 ± 5. 40 %, precision of 79. 07 ± 6. 70 %, recall of 80. 79 ± 7. 87 %, and F1 score of 78. 43 ± 6. 56 %. In comparison, the original Swin-Transformer model produced an AUC of 0. 7011 ± 0. 0334, accuracy of 70. 20 ± 1. 04 %, precision of 68. 58 ± 3. 12 %, recall of 63. 21 ± 3. 60 %, and F1 score of 63. 13 ± 3. 78 %. The differences were statistically significant (P < 0. 05). The KGPT also demonstrated significantly superior performance to radiologists. It effectively classifies ischemic regions in 2DE images, presenting a promising tool for diagnosing myocardial ischemia. The integration of clinical knowledge with Transformer enhances the accuracy and reliability of ischemia classification, potentially revolutionizing the diagnosis and monitoring of myocardial ischemic diseases.

Details DOI

NeurIPS Conference 2024 Conference Paper

FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding

Shuai Yuan
Guancong Lin
Lixian Zhang
Runmin Dong
Jinxiao Zhang
Shuang Chen
Juepeng Zheng
Jie Wang

Fine urban change segmentation using multi-temporal remote sensing images is essential for understanding human-environment interactions in urban areas. Although there have been advances in high-quality land cover datasets that reveal the physical features of urban landscapes, the lack of fine-grained land use datasets hinders a deeper understanding of how human activities are distributed across landscapes and the impact of these activities on the environment, thus constraining proper technique development. To address this, we introduce FUSU, the first fine-grained land use change segmentation dataset for Fine-grained Urban Semantic Understanding. FUSU features the most detailed land use classification system to date, with 17 classes and 30 billion pixels of annotations. It includes bi-temporal high-resolution satellite images with 0. 2-0. 5 m ground sample distance and monthly optical and radar satellite time series, covering 847 km^2 across five urban areas in the southern and northern of China with different geographical features. The fine-grained land use pixel-wise annotations and high spatial-temporal resolution data provide a robust foundation for developing proper deep learning models to provide contextual insights on human activities and urbanization. To fully leverage FUSU, we propose a unified time-series architecture for both change detection and segmentation. We benchmark FUSU on various methods for several tasks. Dataset and code are available at: https: //github. com/yuanshuai0914/FUSU.

PDF Details DOI

AAMAS Conference 2023 Conference Paper

Model-Based Reinforcement Learning for Auto-bidding in Display Advertising

Shuang Chen
Qisen Xu
Liang Zhang
Yongbo Jin
Wenhao Li
Linjian Mo

Real-time bidding (RTB) achieves outstanding success in online display advertising, which has become one of the most influential businesses. Given historical ad impressions under the second price auction mechanism, the advertiser’s optimal bidding strategy is determined by the core parameter corresponding to the optimal solution of a constrained optimization problem. However, the sequentially arrived impressions in online display advertising make it highly non-trivial to obtain the optimal core parameter in advance without knowing the complete impression set. For this reason, recent methods have generally transformed the core parameter determination problem into a sequential parameter adjustment problem and solved it using reinforcement learning (RL). This paper proposes a simple and effective Model-Based Automatic Bidding algorithm, MBAB, which explicitly models the uncertainty of the dynamic auction environment and then uses the dynamic programming algorithm to obtain the current optimal adjustment of the core parameter. MBAB can avoid burdensome simulated environment construction and is more suitable for production deployment without the thorny sim-to-real issue than model-free methods. Furthermore, MBAB uses the optimal bidding formula to carry out coarse-grained modeling of the online market environment to alleviate the scalability problem caused by fine-grained environment modeling of previous model-based methods. In order to accurately describe the impression distribution and non-stationarity of the online market environment, we introduce the probabilistic modeling method and propose a novel monotonicity constraint to regulate the model output. Numerical experiments show that the proposed MBAB substantially outperforms existing baselines on various constrained RTB tasks in the production environment.

PDF

JBHI Journal 2022 Journal Article

Learning From Highly Confident Samples for Automatic Knee Osteoarthritis Severity Assessment: Data From the Osteoarthritis Initiative

Yifan Wang
Zhaori Bi
Yuxue Xie
Tao Wu
Xuan Zeng
Shuang Chen
Dian Zhou

Knee osteoarthritis (OA) is a chronic disease that considerably reduces patients’ quality of life. Preventive therapies require early detection and lifetime monitoring of OA progression. In the clinical environment, the severity of OA is classified by the Kellgren and Lawrence (KL) grading system, ranging from KL-0 to KL-4. Recently, deep learning methods were applied to OA severity assessment to improve accuracy and efficiency. However, this task is still challenging due to the ambiguity between adjacent grades, especially in early-stage OA. Low confident samples, which are less representative than the typical ones, undermine the training process. Targeting the uncertainty in the OA dataset, we propose a novel learning scheme that dynamically separates the data into two sets according to their reliability. Besides, we design a hybrid loss function to help CNN learn from the two sets accordingly. With the proposed approach, we emphasize the typical samples and control the impacts of low confident cases. Experiments are conducted in a five-fold manner on five-class task and early-stage OA task. Our method achieves a mean accuracy of 70. 13% on the five-class OA assessment task, which outperforms all other state-of-art methods. Despite early-stage OA detection still benefiting from the human intervention of lesion region selection, our approach achieves superior performance on the KL-0 vs. KL-2 task. Moreover, we design an experiment to validate large-scale automatic data refining during training. The result verifies the ability to characterize low confidence samples. The dataset used in this paper was obtained from the Osteoarthritis Initiative.

Details DOI

AAAI Conference 2020 Conference Paper

Improving Entity Linking by Modeling Latent Entity Type Information

Shuang Chen
Jinpeng Wang
Feng Jiang
Chin-Yew Lin

Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility. However, the latent entity type information in the immediate context of the mention is neglected, which causes the models often link mentions to incorrect entities with incorrect type. To tackle this problem, we propose to inject latent entity type information into the entity embeddings based on pre-trained BERT. In addition, we integrate a BERT-based entity similarity score into the local context model of a state-of-the-art model to better capture latent entity type information. Our model signiﬁcantly outperforms the state-of-the-art entity linking models on standard benchmark (AIDA-CoNLL). Detailed experiment analysis demonstrates that our model corrects most of the type errors produced by the direct baseline.

PDF Details