Xinyuan Zhang

Although coronary CT angiography (CCTA) is widely utilized for diagnosing coronary artery disease (CAD), automated CCTA image segmentation is frequently hindered by sparse annotations, pseudo-label noise, and under-delineated fine branches. To mitigate these issues, we present PCDW-Net, a semi-supervised segmentation framework that couples perturbation consistency with discrepancy-aware weighting for enhanced label-scarce performance. Utilizing Adaptive Multi-scale Attention Fusion Network (AMAF-Net) as the backbone within a teacher-student architecture, the network applies diverse perturbations to unlabeled samples, leveraging a consistency loss to promote feature invariance. Simultaneously, a pixel-level discrepancy-aware weighting scheme serves to suppress erroneous pseudo-labels. Evaluated on the public ASOCA and private CTA40 datasets using 10% and 20% annotated fractions, the model was evaluated using Dice similarity coefficient (DSC) and Average Symmetric Surface Distance (ASSD). Under the 20% labeling constraint, PCDW-Net yielded a DSC of 85.36% on ASOCA and 83.48% on CTA40, superior to both Mean Teacher (MT) and Mutual Consistency Network+ (MC-Net+). Ablation studies confirmed the efficacy of each module. Overall, the framework effectively leverages unlabeled volumetric data to yield precise vessel boundary delineations.

To address label scarcity and anisotropic structural distortion from unidirectional modeling in semi-supervised cardiac MRI segmentation, we propose DTCL-Swin-UMamba. It features a novel Swin-UMamba backbone and a Multi-directional Vision-based Spatial Synthesis Module (M-VSS). To mitigate the noise sensitivity of traditional Mamba, the weight-shared M-VSS executes eight-directional parallel spatial scanning to build isotropic feature representations, enhancing thin-wall and direction-dependent structure segmentation. The Swin-UMamba backbone fuses global context from Swin Transformer with linear-complexity long-range dependency learning from Mamba. Furthermore, a heterogeneous dual-teacher training strategy—comprising an EMA-updated Swin Transformer teacher and a frozen MS-UNet teacher—provides complementary global and local structural supervision for the student network. Evaluated on the public ACDC and LA datasets, our framework achieves state-of-the-art performance. On ACDC with only 10% labeled images, it achieves Dice scores of 92.15%, 89.57%, and 87.25% for the left ventricle, right ventricle, and myocardium, respectively, with consistent gains demonstrated on the LA dataset.

Driven by artificial intelligence, multi-source sensing, agricultural robots and big data technologies, global agriculture is rapidly upgrading from precision agriculture and agriculture 4.0 to agriculture 5.0. Artificial intelligence has evolved from a single diagnostic tool to an intelligent system that integrates the “perception-decision-execution” process throughout. It is widely applied in crop phenotype analysis, remote sensing monitoring, yield prediction, and autonomous operation of intelligent equipment, etc. This article takes the framework of “intelligent perception-cognitive decision-autonomous execution” to systematically review the core technologies, typical applications, and frontier directions of agricultural artificial intelligence. It focuses on introducing the progress of key technologies such as three-dimensional phenotype, hyperspectral remote sensing, multimodal fusion, and causal machine learning, as well as their value in improving resource utilization efficiency, enhancing climate resilience, and supporting field precision management. At the same time, it points out that current agricultural AI still faces practical bottlenecks such as insufficient generalization ability of models, scarce data and high annotation costs, difficulties in edge deployment, barriers in multi-source data integration, and weak interpretability and engineering reliability. Future research will focus on the construction of closed-loop autonomous farms, the collaboration of agricultural large models and intelligent agents, the construction of data centers and AI and data infrastructure, and the development of green and low-cost AI research. This will provide support for the technological innovation and industrialization implementation of agricultural artificial intelligence.