Author name cluster

Yan Ding

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.

8 papers

2 author rows

JBHI Journal 2026 Journal Article

Automated Screening Network for Fetal Closed Spina Bifida With Semantic Enhancement and Projected Attention

Zhan Gao
Yan Ding
Ningbo Zhu
Hongjie Zhang
Chunlian Wang
Shengli Li
Kenli Li

Closed spina bifida is a high-incidence developmental disorder among rare fetal diseases. Its signs in ultrasound imaging are subtle, making it prone to misdiagnosis and heavily reliant on sonographers' experience. Therefore, we propose a novel semantic enhancement framework incorporating projected attention for the automated screening of closed spina bifida through precise landmark detection. In this method, we utilize a multi-granularity deep supervision and voting mechanism to generate point-specific features and reconstruct saliency maps for each landmark, effectively reducing interference from homogeneous high-echogenic noise in ultrasound images while preserving rich semantic information. Additionally, a coordinate attention projection module is designed to convert the 2D landmark probability maps into one-dimensional vectors, ensuring low computational complexity along with precise coordinate regression. The clinical application potential of this intelligent system is significant, as it facilitates automated fetal spine counting and anatomical measurement, enabling early warnings of diseases based on identified anomalies. Extensive experiments comparing our method with advanced baselines on an in-house dataset and two public datasets demonstrate its clear advantage in computational complexity and accuracy.

Details DOI

EAAI Journal 2025 Journal Article

Drug–target affinity prediction using rotary encoding and information retention mechanisms

Zhiqin Zhu
Yan Ding
Guanqiu Qi
Baisen Cong
Yuanyuan Li
Litao Bai
Xinbo Gao

Drug–target affinity (DTA) prediction has been widely used in pharmaceutical research as a novel and effective method to explore the interaction strength between drugs and targets. However, existing DTA prediction models mainly rely on graphical representations of drug molecules, overlooking the intricate interactions between individual substructures. This limitation impacts both the predictive accuracy and the informational richness within the model nodes. To address these challenges, this paper proposes the Rotary Retention Graph Drug–Target Affinity (RRGDTA) network with rotation and retention mechanisms. The RRGDTA integrates an information interaction module into the extraction of drug and target features across multiple scale levels. This approach enhances the correlation within the graph representation, leading to an optimal feature representation. Furthermore, to tackle the issue of limited relationship between molecular structure and context, a Multi-Scale Interaction module (MSI) is proposed to enhance important features related to both. Additionally, to address inaccuracies in the structural features of drugs and targets, a Rotary Encoding Module (ROE) is proposed, which focuses on nearby contextual information and effectively captures the correlation between them. In order to solve the problem of insufficient information representation, an Association Prediction Module (APM) and an Intra-Mask Retention Module (IMR) are proposed to maximize the retention of drug–target information. The efficacy of the proposed RRGDTA in DTA prediction was validated on the Davis, kinase inhibitors biochemical assays (KIBA) and binding database (BindingDB) datasets. Compared with current baseline models, the proposed model achieved better results across various metrics, demonstrating its superior performance in accurate DTA prediction.

Details DOI

AAAI Conference 2025 Conference Paper

Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding

Xianqiang Gao
Pingrui Zhang
Delin Qu
Dong Wang
Zhigang Wang
Yan Ding
Bin Zhao

3D Object Affordance Grounding aims to predict the functional regions on a 3D object and has laid the foundation for a wide range of applications in robotics. Recent advances tackle this problem via learning a mapping between 3D regions and a single human-object interaction image. However, the geometric structure of the 3D object and the object in the human-object interaction image are not always consistent, leading to poor generalization. To address this issue, we propose to learn generalizable invariant affordance knowledge from multiple human-object interaction images within the same affordance category. Specifically, we introduce the Multi-Image Guided Invariant-Feature-Aware 3D Affordance Grounding (MIFAG) framework. It grounds 3D object affordance regions by identifying common interaction patterns across multiple human-object interaction images. First, the Invariant Affordance Knowledge Extraction Module (IAM) utilizes an iterative updating strategy to gradually extract aligned affordance knowledge from multiple images and integrate it into an affordance dictionary. Then, the Affordance Dictionary Adaptive Fusion Module (ADM) learns comprehensive point cloud representations that consider all affordance candidates in multiple images. Besides, the Multi-Image and Point Affordance (MIPA) benchmark is constructed and our method outperforms existing state-of-the-art methods on various experimental comparisons.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Modular Deep Reinforcement Learning for Multi-Workload Offloading in Edge Networks

Hongchang Ke
Yan Ding
Lin Pan
Yang Chen
Jia Zhao

Dynamic edge networks revolutionize mobile edge computing by enabling real-time applications in intelligent transportation, augmented reality, and industrial Internet of Things (IoT). Efficient workload offloading in dynamic edge networks is crucial for addressing the increasing demands of time-varying workloads while contending with limited computational and communication resources. Existing deep reinforcement learning (DRL)-based offloading decision-making schemes are inadequate for managing scenarios involving multiple workloads and edge servers, particularly when faced with time-varying workload arrivals and fluctuating channel states. To this end, we propose a flexible module weighted fusion DRL framework (DRL-MWF) for scalable and robust multi-workload offloading in edge environments. Unlike traditional monolithic networks, DRL-MWF employs a weighted fusion modular architecture that adapts flexibly to diverse workload distributions. Specifically, DRL-MWF introduces a state representation and normalization strategy to model state and workload characteristics, enabling precise and adaptive decision-making. Furthermore, we design two key mechanisms: a weighted policy correction method to stabilize learning and a prioritized experience replay with weighted importance sampling to accelerate convergence by emphasizing critical transitions. Extensive evaluations on real-world datasets demonstrate that DRL-MWF consistently outperforms state-of-the-art baselines. These results reveal DRL-MWF's potential to transform workload offloading in next-generation edge computing systems, ensuring high performance in dynamic scenarios.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Transforming Gaps into Gains: Bridging Model and Data Heterogeneity in Federated Learning via Knowledge Weak-Aware Zones

Ke Li
Yan Ding
Zhiqin Zhu
Shenhai Zheng

Heterogeneous federated learning enables collaborative training across clients under dual heterogeneity of models and data, posing challenges for effective knowledge transfer. Federated mutual learning employs proxy models to bridge cross-model knowledge exchange; however, existing methods remain limited to direct alignment between the outputs of private and proxy models, ignoring the deep discrepancies in representation and decision spaces between them. Such cognitive biases cause knowledge to be transferred only at shallow levels and trigger performance bottlenecks. To address this, this paper proposes FedKWAZ to identify and exploit Knowledge Weak-Aware Zones (KWAZ)—spatial zones of deep knowledge misalignment between private and proxy models, further refined into Semantic Weak-Aware Zones and Decision Weak-Aware Zones, which characterize cognitive misalignments in representation and decision spaces as focal targets for enhanced bidirectional distillation. FedKWAZ designs a Hierarchical Adaptive Patch Mixing (HAPM) mechanism to generate multiple mixed samples and employs a Knowledge Discrepancy Perceptron (KDP) to select the samples exhibiting the largest representation and decision discrepancies, thereby mining critical KWAZ. These modules are integrated into a two-stage mutual learning framework, achieving global class-level representation-decision consistency alignment and local KWAZ-guided refinement, structurally bridging cognitive biases across heterogeneous mutual learning models. Experimental results on multiple datasets and model configurations demonstrate the superior performance of FedKWAZ.

PDF Details

ICRA Conference 2024 Conference Paper

Towards Optimal Lane-changing Coordination of CAVs in Multi-lane Mixed Traffic Scenarios

Yan Ding
Yijun Mao
Chongshan Jiao
Pengju Ren

Lane changing is a fundamental but challenging operation for moving vehicles. Connected and Automated Vehicles(CAVs) enable autonomous vehicles to cooperate with each other to accomplish the lane changing tasks, profiting from their communication ability. However, dispatching CAVs in mixed traffic remains difficult due to the stochastic behaviors and uncertain intentions of Human-Driven Vehicles(HDVs). To tackle this issue, this paper devises a coordination approach based on Conflict-Based Search(CBS) theory. Firstly, HDVs are accurately modeled as constraints to enable usage of CBS in the mixed traffic. Additionally, virtual goals are introduced to search CAVs’ priority and outlets along with path finding. Furthermore, we optimize the performance of CBS in dense traffic by defining the concept of following vehicles. Experiments show that performance is improved by utilizing new conflict prioritizing rules and a heuristic value calculation method that derived from following vehicles. Finally, we introduce grouping vehicles to extend the proposed method for solving extremely dense and large instances at a scale of more than one hundred without significant loss in efficiency.

Details

JBHI Journal 2023 Journal Article

An IoT-Based Wearable Labor Progress Monitoring System for Remote Evaluation of Admission Time to Hospital

Li Gong
Zhiqing Xiao
Lihua Xu
Yan Ding
Zhuo Zou
Lirong Zheng

Because contractions signal the approach of labor, pregnant women—especially primigravidas (i. e. , women pregnant for the first time)—usually go to the hospital to seek medical intervention when they begin experiencing contractions, which is not conductive to good perinatal outcomes. Conventionally, uterine contraction monitoring requires specialized medical devices and relies on the doctor's clinical experience. Therefore, exploring an objective method to detect labor onset at home and avoid early hospital admission has essential importance. In this article, a labor progress monitoring system based on a sensing device, edge service, and Internet of things (IoT) platform is proposed, aiming to suggest suitable hospital admission times for low-risk primigravidas. The pregnant woman places the sensing device on her abdomen with the help of a belt to detect contraction activities. An intelligent edge service for contraction classification is deployed on a mobile phone. The system's artificial intelligence (AI)–assisted algorithm is lightweight, with 670 kB and 194 kB of memory dedicated to a convolutional neural network and long short-term memory, respectively. It classifies the pregnant woman as deferred admission, optional admission, or recommended admission according to different contraction states. An IoT platform connected to the hospital is implemented, providing professional suggestions from doctors. The test set collected in an emergency clinic shows that the proposed system can reach a classification accuracy of more than 96%. In conclusion, the proposed system enables remote labor progress monitoring at home and avoids early hospital admission.

Details DOI

JBHI Journal 2022 Journal Article

A Cascaded Multi-Task Generative Framework for Detecting Aortic Dissection on 3-D Non-Contrast-Enhanced Computed Tomography

Xiangyu Xiong
Yan Ding
Chuanqi Sun
Zhuoneng Zhang
Xiuhong Guan
Tianjing Zhang
Hao Chen
Hongyan Liu

Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing aortic dissection (AD). However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non-contrast-enhanced CT (NCE-CT) images has poor sensitivity. To address this issue, we propose a novel cascaded multi-task generative framework for AD detection using NCE-CT volumes. The framework includes a 3D nnU-Net and a 3D multi-task generative architecture (3D MTGA). Specifically, the 3D nnU-Net was employed to segment aortas from NCE-CT volumes. The 3D MTGA was then employed to simultaneously synthesize CE-CT volumes, segment true & false lumen, and classify the patient as AD or non-AD. A theoretical formulation demonstrated that the 3D MTGA could increase the Jensen–Shannon Divergence (JSD) between AD and non-AD for each NCE-CT volume, thus indirectly improving the AD detection performance. Experiments also showed that the proposed framework could achieve an average accuracy of 0. 831, a sensitivity of 0. 938, and an F1-score of 0. 847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively. The experimental results indicate that the proposed framework obtains superior performance to state-of-the-art models in AD detection. Thus, it has great potential to reduce the misdiagnosis of AD using NCE-CT in clinical practice. The source codes and supplementary materials for our framework are available at https://github.com/yXiangXiong/CMTGF.

Details DOI