Semi-supervised 3D medical image segmentation under low-label settings often suffers from noisy pseudo labels and anatomically inconsistent predictions, which limit segmentation reliability. Existing methods primarily emphasize training strategies or loss engineering, but they still fail to sufficiently exploit structu...
Si-Bo Qiao, Chao-Ying Yang, Zhi-Yuan Zhao et al.· IEEE journal of biomedical a...· 0 citations
TwinFedSeg, a federated learning framework guided by an anatomical twin state for multimodal medical image segmentation, is proposed, which consistently outperforms representative federated methods.
Hao-Ran Zhou, Ying-Zhi Qin, Peng-Fei Mu et al.· IEEE journal of biomedical a...· 0 citations
New theoretical foundations, models, and architectures that empower explain-able Big Data analytics through Agentic AI are explored, focusing on interpretable reasoning, knowledge graphs, causal inference, human-in-the-loop learning, and visual data storytelling.
Sunil Prajapat, Mohammad Shabaz, Muhammad Asim Saleem et al.· Big Data· 0 citations
An effective Lightweight Multi-scale Deformation Aggregation U-Net (LMDAU-Net), which consists of a Lightweight Local-global Learning Module (LLM) and an Adaptive Interactive Fusion Module (AIF), which utilizes the two branches of the proposed LLM to efficiently learn local fine-grained and global coarse-grained featur...
Jun-Han Hu, Ming Liu, Jing Yang et al.· IEEE journal of biomedical a...· 0 citations
This paper presents a hybrid quantum-safe ABAC framework that integrates fine-grained attribute-based access control with quantum-safe communication techniques and blockchain-assisted attribute management in order to enable secure, efficient, and scalable data sharing in smart healthcare systems.
A Threat-Aware Joint Optimization (TAGO) framework is designed by combining proximal policy optimization for adaptive task offloading and a gradient-based caching update derived from the Frank-Wolfe algorithm to capture spatiotemporal service popularity.
A large language model (LLM) enhanced differentiable trajectory planning framework for IoT-enabled autonomous driving is proposed and a surrounding agent centric data augmentation strategy is introduced to reorganize sur rounding agent trajectories as additional planning supervision, thereby improving the training dist...
Shihao Zhang, Jing Yang, Ziyu Song et al.· IEEE Internet of Things Jour...· 1 citation
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