Accurate vascular segmentation is essential for coronary visualization and the diagnosis of coronary heart disease. This task involves the extraction of sparse tree-like vascular branches from volumetric space. However, existing methods have faced significant challenges due to discontinuous vascular segmentation and missing endpoints. To address this issue, a 3D vision graph neural network framework, named ViG3D-UNet, was introduced. This method integrates 3D graph representation and aggregation within a U-shaped architecture to facilitate continuous vascular segmentation. The ViG3D module captures volumetric vascular connectivity and topology, while the convolutional module extracts fine vascular details. These two branches are combined through channel attention to form the encoder feature. Subsequently, a paperclip-shaped offset decoder minimizes redundant computations in the sparse feature space and restores the feature map size to match the original input dimensions. To evaluate the effectiveness of the proposed approach for continuous vascular segmentation, evaluations were performed on two public datasets, ASOCA and ImageCAS. The segmentation results show that the ViG3D-UNet surpassed competing methods in maintaining vascular segmentation connectivity while achieving high segmentation accuracy.

We address a fundamental challenge in coordinating large-scale 3D drone swarms: how to achieve rapid collective response to environmental stimuli while ensuring group stability and safety. Existing swarm navigation modals often rely on sophisticated individual perception and communication capabilities, which can be computationally expensive and impractical for large swarms. In this paper, we propose the Non-reciprocal Collective Emergent Navigation model (NRCE), a decentralized approach designed for real-world drone flocking in complex environments. Unlike traditional models, our approach leverages localized non-reciprocal interactions, where boundary drones detect environmental stimuli and propagate this information throughout the swarm without directly controlling individual trajectories. Through extensive numerical simulations and physical experiments with up to 28 drones, we demonstrate how this model achieves coordinated collective motion while effectively balancing stability with responsiveness. Our findings reveal two notable insights: (1) intermediate cohesion levels (ωc) optimize collective response—a "Goldilocks zone" where individuals are neither too tightly coupled nor too independent, challenging the conventional wisdom that stronger cohesion always improves coordination; and (2) swarm queue configuration significantly affects optimal interaction parameters, with divergent trends observed between attraction- and repulsion-based coordination mechanisms as layer count increases. These discoveries provide critical design principles for cost-effective, high-density swarm systems while advancing the theoretical understanding of collective dynamics in both artificial and biological systems.

Ant Colony Optimization (ACO) is a prominent swarm intelligence algorithm extensively applied to path planning. However, traditional ACO methods often exhibit shortcomings, such as blind search behavior and slow convergence within complex environments. To address these challenges, this paper proposes the Pheromone-Focused Ant Colony Optimization (PFACO) algorithm, which introduces three key strategies to enhance the problem-solving ability of the ant colony. First, the initial pheromone distribution is concentrated in more promising regions based on the Euclidean distances of nodes to the start and end points, balancing the trade-off between exploration and exploitation. Second, promising solutions are reinforced during colony iterations to intensify pheromone deposition along high-quality paths, accelerating convergence while maintaining solution diversity. Third, a forward-looking mechanism is implemented to penalize redundant path turns, promoting smoother and more efficient solutions. These strategies collectively produce the focused pheromones to guide the ant colony’s search, which enhances the global optimization capabilities of the PFACO algorithm, significantly improving convergence speed and solution quality across diverse optimization problems. The experimental results demonstrate that PFACO consistently outperforms comparative ACO algorithms in terms of convergence speed and solution quality.

Efficient feature learning is considered indispensable for maximizing the representation of scarce information in tiny datasets. However, existing methods are often unable to fully exploit local features and contextual dependencies when dealing with tiny datasets. To overcome this shortcoming, a Cross-Feature Vision Transformer (CF-ViT) was proposed, which decouples local feature refinement from global context modeling and leverages the complementary strengths of CNNs and Transformers. Specifically, a Cross-Scale Fusion (CSF) module was introduced to integrate features from multiple scales, ensuring that cross-scale information is globally embedded. In addition, a Feature Enhancement and Reorganization (FER) module was incorporated into CF-ViT, whereby Transformer outputs are reorganized into 2D feature maps for convolution-based detail enhancement to thoroughly exploit local information. Extensive experiments have demonstrated that CF-ViT consistently surpasses baselines across 4 tiny datasets, reaching a 96.87% (KSDD) Top-1 accuracy with only 29.19 million parameters and 2.67 billion FLOPs. Moreover, a Top-1 accuracy of 85.03% is attained on a real-world tiny dataset of wood surface defect detection, exceeding all baselines. These findings underscore the effectiveness and generalization capability of CF-ViT in capturing fine-grained local details and global context, offering a promising and deployable solution for vision tasks in tiny datasets.

Vision transformers have achieved remarkable success in image classification. The dual-branch vision transformer generates more features by taking advantage of feature fusion. Inspired by this, a dual-branch vision transformer with Real-Time Share feature was proposed during the encoding process for retinal image classification tasks. The approach processes image patches of varying sizes (base and large) through two independent branches and implements multi-stage Real-Time feature fusion via the Real-Time Share feature encoder. This encoder enables the branches to complement each other's features at each encoding stage, facilitating finer feature learning and enhancing the self-attention information passed to subsequent stages. It significantly boosts feature representation and classification performance. Additionally, a straightforward and effective feature fusion method, L-Times Attention Fusion, was proposed: vector concatenation for Real-Time Share feature in the earlier (L-1) encoding stages and element-wise addition for overall feature fusion at the L-th stage, achieving more efficient feature integration. The method was validated on a retinal image dataset. Results show that the approach outperforms the recent Cross-ViT average TOP-1 Acc by 5.61% with lower FLOPs and model parameters, without relying on pre-trained weights, highlighting stronger self-learning feature capabilities and reduced reliance on extensive pre-training data.

Lesion segmentation plays a crucial role in various medical image analyses, which not only improves the efficiency in clinical diagnosis but also assists in detecting early symptoms of various diseases. Most existing studies focus on directly extracting lesion information from specific types of medical images with pre-trained weights, often neglecting the underlying topological and pathological causes which lead to these lesions. Furthermore, they overlook to capture general anatomical features among lesions, which are related to the distribution of lesions, and thus the model is poorly generalized in different medical datasets. Inspired by these insights, we propose a Triple Fusion Net (TF-Net), a network structure divided into three branches: left, middle and right. The left and right branches are designed to extract lesion features and associated topological style features within various medical images, respectively. And these features are further fused and modeled in the middle branch. The proposed structure of triple branches for features fusing effectively learns multi-feature information and improves the performance of TF-Net. And our work experiments validate various feature fusion methods in the middle branch, including channel-wise concatenation, element-wise addition, attention gate, and transformer encoder block. Without using pre-trained weights in our network, the transformer encoder block performs best on some tasks of DDR and surpasses other pre-trained models. Channel concatenation exhibits performance close to other pre-trained models in both the IDRiD, Kvasir-Seg and TN3K. Attention gate fusion also shows competitive results in thyroid ultrasound segmentation. Our approach, leveraging a unique network structure and four different feature fusion methods, demonstrates remarkable generality across a spectrum of medical image segmentation tasks.

Peripheral waveform analysis (PWA), which is generally used to reveal hidden health status information from peripheral pulse signals, typically involves three procedures: signal preprocessing, feature extraction, and pattern classification. With the advancement of data-driven deep neural network methodologies, feature extraction and pattern classification have progressively converged into end-to-end neural networks, where the final layer of the network is equivalent to conventional pattern classifiers. However, the performance of deep learning models heavily relies on the quality of the dataset, rendering data signal preprocessing a crucial component. This study proposes a framework that integrates signal preprocessing, feature extraction, and pattern classification into a unified learning approach using heuristic algorithms, enabling the automatic discovery of optimal data encoding methods and their corresponding models. Initially, the search space is defined based on parameters relevant to signal preprocessing, and a fitness function is constructed utilizing CNN. Subsequently, the optimal combination of data preprocessing and CNN is determined through the heuristic algorithm Particle Swarm Optimization (PSO). The proposed method was evaluated in the dataset comprising authentic clinical cases of type 2 diabetes screening involving approximately 200 volunteers. The model derived from this framework demonstrates the capability to effectively discriminate between healthy volunteers and those with diabetes, achieving the highest accuracy of 93.6%. Compared to state-of-the-art algorithms, the proposed model was shown to be competitive in both accuracy and time cost.

We present a method for the fast computation of the intersection between a ray and the geometry of a scene. The scene geometry is simplified with a 2D array of voxelizations computed from different directions, sampling the space of all possible directions. The 2D array of voxelizations is compressed using a vector quantization approach. The ray-scene intersection is approximated using the voxelization whose rows are most closely aligned with the ray. The voxelization row that contains the ray is looked up, the row is truncated to the extent of the ray using bit operations, and a truncated row with non-zero bits indicates that the ray intersects the scene. We support dynamic scenes with rigidly moving objects by building a separate 2D array of voxelizations for each type of object, and by using the same 2D array of voxelizations for all instances of an object type. We support complex dynamic scenes and scenes with deforming geometry by computing and rotating a single voxelization on the fly. We demonstrate the benefits of our method in the context of interactive rendering of scenes with thousands of moving lights, where we compare our method to ray tracing, to conventional shadow mapping, and to imperfect shadow maps.