Ziyang Wang

Physically Constrained Dual-Branch Front-End Optimization for DDH-Oriented Surgical Robot Navigation

Jiabao Li, Ming Zhu, Chengjun Wang, Kang Xie et autres

Surgical robot navigation in a restricted pelvic workspace requires accurate target localization, directional consistency and robot-feasible execution. This study proposes a physically constrained dual-branch front-end network (PCD-Net) for DDH-oriented navigation using public CT-derived pelvis geometries. PCD-Net maps a 21-dimensional input comprising the …

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Compatibility and Interfacial Bonding Mechanism Between Portland Cement and Alkali-Activated Materials in Gradient Cementitious Composites: A Comparative Study of Mixed and Layered Casting Methods

Baoxuan Dou, Guodong Huang, Zhihao Liu, Fengan Zhang et autres

An innovative cement and alkali-activated gradient cementitious composite was fabricated via mixed and layered casting methods. The influence of casting method and mix proportion on mechanical performance was systematically investigated, and the compatibility and synergistic mechanisms were characterized by XRD, FT–IR, and …

cn (code pays fourni par la source)

Semi-SwinUNeTR: Towards 3D Swin Vision Transformer-Based UNet for Medical Image Segmentation with Limited Annotations

Yinbing Tian, Ziyang Wang, Li Guo

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for computer-assisted diagnosis, treatment planning, and disease monitoring. However, brain tumors usually exhibit irregular, heterogeneous, and multi-scale spatial patterns with complex and ambiguous boundaries. At the same time, the performance of …

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Query-Specific Context-Enhanced Representation Learning for Temporal Knowledge Graph Reasoning

Yu Gao, Qingtian Zeng, Weijian Ni, Cheng Cheng et autres

Temporal Knowledge Graph Reasoning (TKGR) aims to leverage historical information to predict future facts. However, most existing methods learn entity embeddings shared across all queries at the same timestamp, without considering query-specific context. To tackle these problems, we propose Query-Specific Context-Enhanced Representation …

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VMambaMorph: A 3D Multi-Modality Deformable Image Registration Framework Based on Visual State Space Model with Cross-Scan Module

Ziyang Wang, Jian-Qing Zheng, Yongxiang Lei, Tianli Tao et autres

Image registration, a critical process in medical imaging, involves aligning different sets of medical imaging data into a single unified coordinate system. Deep learning networks, such as the Convolutional Neural Network (CNN)-based VoxelMorph, Vision Transformer (ViT)-based TransMorph, and State Space Model (SSM)-based …

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On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations

Ziyang Wang

Satellite constellations are transforming space systems from isolated spacecraft into networked, software-defined platforms capable of on-orbit perception, decision making, and adaptation. Yet many of the existing AI studies remain centered on single-satellite inference, while constellation-scale autonomy introduces fundamentally new algorithmic requirements: learning …

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Trustworthy Longitudinal Brain MRI Completion: A Deformation-Based Approach with KAN-Enhanced Diffusion Model

Tianli Tao, Ziyang Wang, Delong Yang, Han Zhang et autres

Longitudinal brain MRI is essential for lifespan study, yet high attrition rates often lead to missing data, complicating analysis. Deep generative models have been explored, but most rely solely on image intensity, leading to two key limitations: 1) the fidelity or trustworthiness …

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MAC-S$^{3}$Robo: Mamba-Driven Contrastive Semi-Supervised Surgical Robot Segmentation for Minimally Invasive Surgery

Chengyi Zhang, Zhihao Chen, Yiyuan Ge, Zhihao Guo et autres

Accurate and reliable surgical robot segmentation during endoscopic surgery is critical for improving robotic perception, task automation, and patient safety. Current deep learning approaches rely heavily on supervised training, requiring extensive, manually annotated datasets, which are costly and difficult to obtain in …

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Query, Align, and Distill: Navigation-Aware Cross-Modal Interaction for Efficient Vision-and-Language Navigation

Zhihao Chen, Yiyuan Ge, Ziyang Wang, Pu Cao et autres

Recent large-scale Vision-and-Language Navigation (VLN) models deliver strong accuracy but remain costly to deployment due to heavy parameters and computation. We tackle efficient VLN in two steps. First, we build a high-performing teacher that makes navigation evidence selection explicit and compressible. Concretely, …

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HMSA-SegNet: A Parameter-Efficient Architecture with Hierarchical Multi-Scale Attention for Medical Image Segmentation

Boyang Yu, Hong‐Seng Gan, Zijun Chen, Ziyang Wang

Medical image segmentation plays a critical role in diagnostic and therapeutic applications, requiring high accuracy while maintaining computational efficiency. Traditional CNNbased methods such as U-Net effectively captured spatial features but often struggled with long-range dependencies. Transformerbased models improved global context modeling but …

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VividFace: High-Quality and Efficient One-Step Diffusion For Video Face Enhancement

Shulian Zhang, Yong‐Xin Guo, Long Peng, Ziyang Wang et autres

Video Face Enhancement (VFE) aims to restore high-quality facial regions from degraded video sequences, enabling a wide range of practical applications. Despite substantial progress in the field, current methods that primarily rely on video super-resolution and generative frameworks continue to face three …

S4RoboFormer: Scribble-Supervised Surgical Robotic Segmentation Transformer via Augmented Consistency Training

Ziyang Wang, Tianxiang Chen, Zi Ye, Yiyuan Ge et autres

Advancements in deep learning for surgical instrument segmentation have notably improved the proficiency, safety, and efficacy of minimally invasive robotic surgeries. The effectiveness of deep learning, however, is contingent upon the availability of large datasets for training, which are often associated with …

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