Selected work

Representative Papers

Can We Model the Artifacts Explicitly? Disentangle Artifacts via Pairwise Edit Relations for Image Manipulation Localization

Oct 06, 2026

This study addresses the performance bottleneck in image forgery localization caused by the implicit modeling of artifacts, reformulating the task as a latent variable problem and proposing a two-stage paradigm to explicitly model tampering artifacts. Methodologically, it introduces paired artifact learning alongside standard localization strategies, and designs an edit-relation-based feature disentanglement mechanism that effectively separates content from artifact representations. Additionally, a large-scale dataset, EditGroup-45K, is constructed. Experimental results demonstrate that the proposed approach not only significantly enhances the localization performance of various mainstream models but also thoroughly validates its capability to explicitly capture tampering artifacts.

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DR-IPC: Disturbance-Resilient Integrated Planning and Control for LiDAR-Based Quadrotor Navigation

Oct 02, 2026

This study addresses the limited robustness of LiDAR-equipped quadrotors under complex disturbances caused by the separation of planning and control. To this end, a disturbance-robust integrated planning and control (DR-IPC) navigation framework is proposed. This method fuses path guidance with nonlinear model predictive control (NMPC), eliminating the need for independent trajectory optimization. By unifying dynamics, constraints, and safety corridors, it directly generates angular velocity and thrust commands. Furthermore, an interconnected extended Kalman filter and a nonlinear disturbance observer are synergistically employed to achieve precise state estimation and disturbance compensation. Experimental results demonstrate that the task completion rate increases from 10% to 90%, the altitude root mean square error (RMSE) is reduced to 0.01 m, and the onboard computational frequency reaches 100 Hz.

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RouteRelay: Event-Triggered Cross-Layer Route Reuse for Efficient Dynamic Sparse Attention

Sep 07, 2026

本文提出RouteRelay方法，通过跨层复用路由元数据减少动态稀疏注意力中的重复计算，提高效率。

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Multi-Contrast Fusion Module: An attention mechanism integrating multi-contrast features for fetal torso plane classification

Aug 13, 2025

Fine-grained anatomical structure identification in fetal trunk standard-plane ultrasound images remains challenging due to inherently low contrast and blurred texture. Method: This paper proposes a lightweight multi-contrast fusion module that introduces a novel multi-contrast attention mechanism at the network’s lower layers. By adaptively weighting low-level features extracted under multiple contrast enhancements, the module significantly improves modeling of subtle anatomical details with negligible parameter overhead. It operates directly on raw ultrasound data to enhance feature representation of clinically critical regions. Contribution/Results: Evaluated on a fetal trunk standard-plane dataset, the method achieves substantial improvements in classification accuracy—particularly for key structures including the heart, spine, and stomach bubble—thereby enhancing diagnostic consistency and reliability. The approach demonstrates clear clinical utility in automated fetal anatomy assessment.

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Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging

Jan 01, 2025

To address low detection accuracy, high diagnostic burden on clinicians, and elevated misdiagnosis rates in prenatal ultrasound screening for fetal central nervous system (CNS) malformations, this study develops the first multi-center deep learning model covering the entire gestational period for automated detection and classification of four canonical CNS anomalies: anencephaly, encephalocele, holoprosencephaly, and spina bifida. Methodologically, we integrate a ResNet-based architecture, multi-center collaborative training, and class activation mapping (CAM) for interpretable lesion localization. Our contribution is the first demonstration of robust, gestational-week-agnostic CNS anomaly classification with intrinsic interpretability. The model achieves 94.5% patient-level accuracy and 99.3% AUROC. A retrospective reader study demonstrates significant improvements in radiologists’ diagnostic accuracy and efficiency, alongside substantial reduction in misdiagnosis rates.

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Recent publications

Latest Papers

Can We Model the Artifacts Explicitly? Disentangle Artifacts via Pairwise Edit Relations for Image Manipulation Localization

Oct 06, 2026

This study addresses the performance bottleneck in image forgery localization caused by the implicit modeling of artifacts, reformulating the task as a latent variable problem and proposing a two-stage paradigm to explicitly model tampering artifacts. Methodologically, it introduces paired artifact learning alongside standard localization strategies, and designs an edit-relation-based feature disentanglement mechanism that effectively separates content from artifact representations. Additionally, a large-scale dataset, EditGroup-45K, is constructed. Experimental results demonstrate that the proposed approach not only significantly enhances the localization performance of various mainstream models but also thoroughly validates its capability to explicitly capture tampering artifacts.

0 citationsRead paper

DR-IPC: Disturbance-Resilient Integrated Planning and Control for LiDAR-Based Quadrotor Navigation

Oct 02, 2026

This study addresses the limited robustness of LiDAR-equipped quadrotors under complex disturbances caused by the separation of planning and control. To this end, a disturbance-robust integrated planning and control (DR-IPC) navigation framework is proposed. This method fuses path guidance with nonlinear model predictive control (NMPC), eliminating the need for independent trajectory optimization. By unifying dynamics, constraints, and safety corridors, it directly generates angular velocity and thrust commands. Furthermore, an interconnected extended Kalman filter and a nonlinear disturbance observer are synergistically employed to achieve precise state estimation and disturbance compensation. Experimental results demonstrate that the task completion rate increases from 10% to 90%, the altitude root mean square error (RMSE) is reduced to 0.01 m, and the onboard computational frequency reaches 100 Hz.

0 citationsRead paper

RouteRelay: Event-Triggered Cross-Layer Route Reuse for Efficient Dynamic Sparse Attention

Sep 07, 2026

本文提出RouteRelay方法，通过跨层复用路由元数据减少动态稀疏注意力中的重复计算，提高效率。

0 citationsRead paper

Multi-Contrast Fusion Module: An attention mechanism integrating multi-contrast features for fetal torso plane classification

Aug 13, 2025

Fine-grained anatomical structure identification in fetal trunk standard-plane ultrasound images remains challenging due to inherently low contrast and blurred texture. Method: This paper proposes a lightweight multi-contrast fusion module that introduces a novel multi-contrast attention mechanism at the network’s lower layers. By adaptively weighting low-level features extracted under multiple contrast enhancements, the module significantly improves modeling of subtle anatomical details with negligible parameter overhead. It operates directly on raw ultrasound data to enhance feature representation of clinically critical regions. Contribution/Results: Evaluated on a fetal trunk standard-plane dataset, the method achieves substantial improvements in classification accuracy—particularly for key structures including the heart, spine, and stomach bubble—thereby enhancing diagnostic consistency and reliability. The approach demonstrates clear clinical utility in automated fetal anatomy assessment.

0 citationsRead paper

Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging

Jan 01, 2025

To address low detection accuracy, high diagnostic burden on clinicians, and elevated misdiagnosis rates in prenatal ultrasound screening for fetal central nervous system (CNS) malformations, this study develops the first multi-center deep learning model covering the entire gestational period for automated detection and classification of four canonical CNS anomalies: anencephaly, encephalocele, holoprosencephaly, and spina bifida. Methodologically, we integrate a ResNet-based architecture, multi-center collaborative training, and class activation mapping (CAM) for interpretable lesion localization. Our contribution is the first demonstration of robust, gestational-week-agnostic CNS anomaly classification with intrinsic interpretability. The model achieves 94.5% patient-level accuracy and 99.3% AUROC. A retrospective reader study demonstrates significant improvements in radiologists’ diagnostic accuracy and efficiency, alongside substantial reduction in misdiagnosis rates.

0 citationsRead paper