Infrared Small Target Detection (IRSTD) requires strong target-level detection capability, which depends on effective modeling of long-range global dependencies. This demand has driven the transition from CNN-based approaches to Transformer-based architectures. Although Transformers improve global context modeling, their high computational cost limits practical deployment. Recent advances in Mamba enable efficient long-range dependency modeling with reduced complexity, offering a promising alternative that alleviates the efficiency limitations of Transformers while preserving target-level detection performance. However, Mamba is not inherently tailored for IRSTD, as it lacks explicit mechanisms for capturing fine-grained local details and modeling background variations across multiple spatial scales. To address these limitations, we propose MCFNet, an encoder-decoder framework that integrates Mamba to enhance target-level detection performance with moderate computational cost. MCFNet introduces a Detail-Capturable Convolution Block to strengthen local detail perception and a Multi-scale Contextual Mamba Block to improve background modeling across different scales. While the resulting dual-branch design enhances both global semantics and local details, it also introduces challenges in feature fusion. To this end, a Feature Fusion Decoding Module is further proposed to enable effective collaboration between global and local representations. Extensive experiments on multiple public IRSTD benchmark datasets demonstrate that MCFNet consistently outperforms existing methods in both pixel-level and target-level metrics, achieving higher detection accuracy with reduced false alarms. The code of our model is available at: https://github.com/Fihven/MCFNet.

In modern computer-aided design (CAD), Boundary Representation (B-rep) is a widely used geometric modeling technique in industrial design and manufacturing. However, existing B-rep generation methods, which rely on tree-based hierarchies to represent and generate B-reps, fail to fully exploit the inherent graph structure of B-reps, resulting in suboptimal efficiency and model quality. To address this issue, we propose BRep-GD, a graph diffusion-based model specifically designed for B-rep generation. Unlike prior methods, BRep-GD treats B-reps as graphs, where nodes represent face elements, and edges represent boundary and vertex elements. By utilizing a continuous topological graph data structure, BRep-GD overcomes the challenges associated with directly applying graph diffusion models to B-rep generation. Specifically, BRep-GD introduces a graph diffusion method tailored to the features of CAD data, generating faces and edges sequentially. During edge generation, continuous topology decoupling is employed to avoid the need for global attention calculations, reducing computational complexity while ensuring geometric consistency and high-quality results. Experimental results demonstrate that BRep-GD outperforms existing state-of-the-art methods in both unconditional and class-conditional generation tasks, particularly in generating watertight solids and handling complex geometries. It significantly reduces isolated or inconsistent geometric components and improves generation efficiency.

Functional magnetic resonance imaging (fMRI) is a powerful tool for probing brain function, yet reliable clinical diagnosis is hampered by low signal-to-noise ratios, inter-subject variability, and the limited frequency awareness of prevailing CNN- and Transformer-based models. Moreover, most fMRI datasets lack textual annotations that could contextualize regional activation and connectivity patterns. We introduce RTGMFF, a framework that unifies automatic ROI-level text generation with multimodal feature fusion for brain-disorder diagnosis. RTGMFF consists of three components: (i) ROI-driven fMRI text generation deterministically condenses each subject's activation, connectivity, age, and sex into reproducible text tokens; (ii) Hybrid frequency-spatial encoder fuses a hierarchical waveletmamba branch with a cross-scale Transformer encoder to capture frequency-domain structure alongside long-range spatial dependencies; and (iii) Adaptive semantic alignment module embeds the ROI token sequence and visual features in a shared space, using a regularized cosine-similarity loss to narrow the modality gap. Extensive experiments on the ADHD-200 and ABIDE benchmarks show that RTGMFF surpasses current methods in diagnostic accuracy, achieving notable gains in sensitivity, specificity, and area under the ROC curve. Code is available at https://github.com/BeistMedAI/RTGMFF.

Brain tumor segmentation from low-quality MRI scans poses significant challenges, particularly in sub-Saharan Africa, where the scans frequently suffer from low resolution and artifacts. Such degradations introduce substantial domain shifts that hinder the effectiveness of existing test-time adaptation (TTA) methods, largely due to catastrophic forgetting and the unreliability of pseudo-labels. In response, we introduce DRTTA, a dynamic and robust framework designed for effective test-time adaptation. This method maintains essential knowledge from the source domain by freezing certain parameters and utilizing adaptive BatchNorm, allowing for successful alignment with the target domain. During inference, DR-TTA employs a learnable augmentation strategy that is optimized to simulate distortions specific to the target domain. Additionally, a hybrid loss function incorporating geometric constraints is used to filter out unreliable pseudo-labels, thus stabilizing the training process. Our extensive experiments on the BraTS-SSA and BraTS-SIM datasets demonstrate that DR-TTA significantly surpasses existing state-of-the-art methods across key performance metrics. This advancement provides a viable solution for deploying brain tumor segmentation technology in real-world scenarios, particularly within resource-limited environments. Our source code is available at https://github.com/baiyou1234/DR-TTA.

Recently, Electroencephalography (EEG) has been increasingly utilized in driving fatigue detection tasks. However, the inter-subject variabilities in EEG data render models trained on one subject ineffective for being directly applied to others. Transfer learning has been widely used to address this issue, but most existing transfer learning algorithms primarily focused on classification tasks. Therefore, we propose a transfer regression model for EEG-based driving fatigue detection, whose core idea is to learn the weights of models from various source domain data and a base model from target domain training data through an attention network. By assembling models trained on different domain data, predictions are obtained. We conducted experiments on the two subsets of the benchmark SEED-VIG dataset, and the results demonstrate that our transfer regression model effectively enhances the driving fatigue detection performance. The source code is available from https://github.com/SunseaIU/ATR-EEG.

Alzheimer’s Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. Early diagnosis is challenging due to subtle symptoms and varied presentations, often leading to misdiagnosis with traditional unimodal diagnostic methods due to their limited scope. This study introduces an advanced multimodal classification model that integrates clinical, cognitive, neuroimaging, and EEG data to enhance diagnostic accuracy. The model incorporates a feature tagger with a tabular data coding architecture and utilizes the TimesBlock module to capture intricate temporal patterns in Electroencephalograms (EEG) data. By employing Cross-modal Attention Aggregation module, the model effectively fuses Magnetic Resonance Imaging (MRI) spatial information with EEG temporal data, significantly improving the distinction between AD, Mild Cognitive Impairment, and Normal Cognition. Simultaneously, we have constructed the first AD classification dataset that includes three modalities: EEG, MRI, and tabular data. Our innovative approach aims to facilitate early diagnosis and intervention, potentially slowing the progression of AD. The source code and our private ADMC dataset are available at https://github.com/JustlfC03/MSTNet.

Computer-Aided Design (CAD) generative modeling is driving significant innovations across industrial applications. Recent works have shown remarkable progress in creating solid models from various inputs such as point clouds, meshes, and text descriptions. However, these methods fundamentally diverge from traditional industrial workflows that begin with 2D engineering drawings. The automatic generation of parametric CAD models from these 2D vector drawings remains underexplored despite being a critical step in engineering design. To address this gap, our key insight is to reframe CAD generation as a sequence-to-sequence learning problem where vector drawing primitives directly inform the generation of parametric CAD operations, preserving geometric precision and design intent throughout the transformation process. We propose Drawing2CAD, a framework with three key technical components: a network-friendly vector primitive representation that preserves precise geometric information, a dual-decoder transformer architecture that decouples command type and parameter generation while maintaining precise correspondence, and a soft target distribution loss function accommodating inherent flexibility in CAD parameters. To train and evaluate Drawing2CAD, we create CAD-VGDrawing, a dataset of paired engineering drawings and parametric CAD models, and conduct thorough experiments to demonstrate the effectiveness of our method. Code and dataset are available at https://github.com/lllssc/Drawing2CAD.

Abstract Does a query image with much higher resolution than that of the gallery image also affect the pedestrian re‐identification performance? If so, and how does it affect performance? The study proposes a novel framework for performing high‐resolution image reconstruction and pedestrian re‐identification tasks, independent of the query image resolution. More precisely, an end‐to‐end trainable Resolution Independent person Re‐identification network is proposed. It is composed of our designed Cross‐Resolution GAN and Embedding Batch Normalisation layers. The model is then compared with the traditional low‐resolution pedestrian recognition algorithm and the hybrid method of high‐resolution reconstruction and pedestrian re‐identification. The results demonstrate that the proposed method outperforms the state‐of‐the‐art methods in the pedestrian re‐identification task based on our expanded benchmark dataset. It also reaches an equivalent performance to the existing methods in the high‐resolution image reconstruction task.

The incidence and mortality rates of malignant tumors, such as acute leukemia, have risen significantly. Clinically, hospitals rely on cytological examination of peripheral blood and bone marrow smears to diagnose malignant tumors, with accurate blood cell counting being crucial. Existing automated methods face challenges such as low feature expression capability, poor interpretability, and redundant feature extraction when processing high-dimensional microimage data. We propose a novel fine-grained classification model, SCKansformer, for bone marrow blood cells, which addresses these challenges and enhances classification accuracy and efficiency. The model integrates the Kansformer Encoder, SCConv Encoder, and Global-Local Attention Encoder. The Kansformer Encoder replaces the traditional MLP layer with the KAN, improving nonlinear feature representation and interpretability. The SCConv Encoder, with its Spatial and Channel Reconstruction Units, enhances feature representation and reduces redundancy. The Global-Local Attention Encoder combines Multi-head Self-Attention with a Local Part module to capture both global and local features. We validated our model using the Bone Marrow Blood Cell Fine-Grained Classification Dataset (BMCD-FGCD), comprising over 10,000 samples and nearly 40 classifications, developed with a partner hospital. Comparative experiments on our private dataset, as well as the publicly available PBC and ALL-IDB datasets, demonstrate that SCKansformer outperforms both typical and advanced microcell classification methods across all datasets.

The reuse of 3D CAD models is crucial for industrial manufacturing because it shortens development cycles and reduces costs. Significant progress has been made in deep learning-based 3D model retrievals. There are many representations for 3D models, among which the multi-view representation has demonstrated a superior retrieval performance. However, directly applying these 3D model retrieval approaches to 3D CAD model retrievals may result in issues such as the loss of the engineering semantic and structural information. In this paper, we find that multiple views and B-rep can complement each other. Therefore, we propose the view graph neural network (VGNet), which effectively combines multiple views and B-rep to accomplish 3D CAD model retrieval. More specifically, based on the characteristics of the regular shape of 3D CAD models, and the richness of the attribute information in the B-rep attribute graph, we separately design two feature extraction networks for each modality. Moreover, to explore the latent relationships between the multiple views and B-rep attribute graphs, a multi-head attention enhancement module is designed. Furthermore, the multimodal fusion module is adopted to make the joint representation of the 3D CAD models more discriminative by using a correlation loss function. Experiments are carried out on a real manufacturing 3D CAD dataset and a public dataset to validate the effectiveness of the proposed approach.

Abstract With the widespread application of CAD technology in the industrial manufacturing sector, the efficient retrieval of target models has become a critical research topic. Despite the outstanding performance of traditional supervised retrieval methods, their reliance on large amounts of labeled data significantly limits practical applications. Data labeling is not only time-consuming and costly but also difficult to ensure accuracy and consistency. To tackle this issue, this paper introduces CADGCL, an unsupervised method for retrieving CAD models based on boundary representations. The proposed method transforms CAD models represented by boundary representations into B-rep attributed graphs that integrate geometric information and topological structures. Graph contrastive learning facilitates unsupervised CAD model retrieval. To overcome the limitations of traditional GCL methods in data augmentation and negative sampling, two novel strategies are introduced: an edge perturbation strategy based on Edge Betweenness Centrality and a negative sampling strategy based on the Beta Mixture Model. These strategies effectively improve the performance of contrastive learning. Experimental results show that the proposed method outperforms existing approaches in mAP and F1 scores under unsupervised scenarios, validating its potential for applications in industrial manufacturing.

The fusion of multimodal medical data is crucial for helping doctors make accurate treatment decisions. For example, combining Computed Tomography Pulmonary Angiography (CTPA) with Electronic Health Records (EHR) can significantly improve the accuracy of Pulmonary Embolism (PE) detection, thereby increasing patient survival rates. Although multimodal learning has advantages in PE diagnosis, the heterogeneity of multimodal data poses a significant challenge to accurate diagnosis. The natural semantic and structural differences between data modalities make it difficult to effectively integrate their information. In addition, within a single modality, the existence of redundant and irrelevant information introduces unnecessary variability, making the data more complex, and making stable diagnosis challenging. To address these issues, we propose a new framework called CCLNet, which includes a contrastive learning component for addressing inter-modality heterogeneity and a causal learning component for handling intra-modality heterogeneity. Specifically, we achieve precise alignment between visual and tabular modalities by using global-level information to soften labels during contrastive learning. In addition, by using causal intervention methods to eliminate the influence of heterogeneous factors within the modality, we can accurately reveal the causal relationship between features and targets, thereby improving the accuracy and stability of the model. Experimental results demonstrate that our method performs excellently, achieving the best results. Our code is available at https://github.com/LeavingStarW/CLPE.

Pulmonary embolism (PE) is a prevalent lung disease that can lead to right ventricular hypertrophy and failure in severe cases, ranking second in severity only to myocardial infarction and sudden death. Pulmonary artery CT angiography (CTPA) is a widely used diagnostic method for PE. However, PE detection presents challenges in clinical practice due to limitations in imaging technology. CTPA can produce noises similar to PE, making confirmation of its presence time-consuming and prone to overdiagnosis. Nevertheless, the traditional segmentation method of PE can not fully consider the hierarchical structure of features, local and global spatial features of PE CT images. In this paper, we propose an automatic PE segmentation method called SCUNet++ (Swin Conv UNet++). This method incorporates multiple fusion dense skip connections between the encoder and decoder, utilizing the Swin Transformer as the encoder. And fuses features of different scales in the decoder subnetwork to compensate for spatial information loss caused by the inevitable downsampling in Swin-UNet or other state-of-the-art methods, effectively solving the above problem. We provide a theoretical analysis of this method in detail and validate it on publicly available PE CT image datasets FUMPE and CAD-PE. The experimental results indicate that our proposed method achieved a Dice similarity coefficient (DSC) of 83.47% and a Hausdorff distance 95th percentile (HD95) of 3.83 on the FUMPE dataset, as well as a DSC of 83.42% and an HD95 of 5.10 on the CAD-PE dataset. These findings demonstrate that our method exhibits strong performance in PE segmentation tasks, potentially enhancing the accuracy of automatic segmentation of PE and providing a powerful diagnostic tool for clinical physicians. Our source code and new FUMPE dataset are available at https://github.com/JustlfC03/SCUNet-plusplus.

Multimodal image fusion plays an essential role in medical image analysis and application, where computed tomography (CT), magnetic resonance (MR), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) are commonly-used modalities, especially for brain disease diagnoses. Most existing fusion methods do not consider the characteristics of medical images, and they adopt similar strategies and assessment standards to natural image fusion. While distinctive medical semantic information (MS-Info) is hidden in different modalities, the ultimate clinical assessment of the fusion results is ignored. Our MsgFusion first builds a relationship between the key MS-Info of the MR/CT/PET/SPECT images and image features to guide the CNN feature extractions using two branches and the design of the image fusion framework. For MR images, we combine the spatial domain feature and frequency domain feature (SF) to develop one branch. For PET/SPECT/CT images, we integrate the gray color space feature and adapt the HSV color space feature (GV) to develop another branch. A classification-based hierarchical fusion strategy is also proposed to reconstruct the fusion images to persist and enhance the salient MS-Info reflecting anatomical structure and functional metabolism. Fusion experiments are carried out on many pairs of MR-PET/SPECT and MR-CT images. According to seven classical objective quality assessments and one new subjective clinical quality assessment from 30 clinical doctors, the fusion results of the proposed MsgFusion are superior to those of the existing representative methods.

Performing 3-dimensional computer-aided design (3D-CAD) model classification , retrieval, and reuse is of vital importance in industrial manufacturing, as it considerably shortens the engineering development cycle and reduces development costs. Although existing 3D model classification and retrieval methods achieve satisfactory performance when operating on meshes or point clouds, they cannot be applied directly to 3D-CAD models, which are generally represented by the boundary representation (B-rep). To address this issue, and to fully exploit the topology of B-rep, a graph structure descriptor called B-rep graph is proposed to pre-process B-rep data, and to extract the precise topological and geometric features from 3D-CAD models. Meanwhile, a novel efficient neural network called FuS-GCN, based on graph convolutional networks (GCNs), is designed to handle this graph data. To better extract the graph features and to improve the effect of pooling, the self-attention mechanism and feature fusion are incorporated into the pooling layer, yielding the proposed fusion self-attention graph pooling (FuSPool) algorithm. Finally, we demonstrate the effectiveness of FuS-GCN on 3D-CAD model data, while outperforming alternative 3D shape descriptors such as point clouds, voxels, and meshes.

Regarding programmable logic controller (PLC) development, improving programming efficiency and encouraging code exchange and software reuse are necessary to increase the productivity and safety of smart manufacturing and industrial automation. However, differences between implementations are significant even though all manufacturers claim to conform to the IEC 61131-3 and IEC 61131-10 standards, resulting in incompatibilities inside heterogeneous systems, preventing projects from interoperating between vendors. In this article, we present an approach that utilizes an ontology-based semantic model named OntoPLC to enable automatic porting of PLC projects between development environments and also prevent significant information loss during the translation process. High-level semantics of PLC projects are added into OntoPLC including software resources, which can be required for software reuse leveraging semantic query. To demonstrate the usefulness of the ontology model, the proposed methodology is applied to a turbine-driven boiler feed pump control module.

Non-negative matrix factorization (NMF) is an efficient model in learning parts-based data representation. Since the local geometrical structure can be effectively modeled by a nearest neighbor graph, the graph regularized NMF (GNMF) was proposed to make the learned representation more faithfully and better characterize the intrinsic structure of data. However, GNMF shares a similar paradigm with most of existing graph-based learning models which perform learning tasks on a fixed input graph. In this paper, we propose a new Flexible NMF model with adaptively learned Graph regularization (FNMFG) in which the graph is jointly learned with simultaneous performing the matrix factorization. An efficient iterative method with guaranteed convergence and relative low complexity is developed to optimize the FNMFG objective. Experiments compare FNMFG method with state-of-the-art algorithms and demonstrate its improved performance.

Non-negative Matrix Factorization (NMF) is a popular model in machine learning, which can learn parts-based representation by seeking for two non-negative matrices whose product can best approximate the original matrix. However, the manifold structure is not considered by NMF and many of the existing work use the graph Laplacian to ensure the smoothness of the learned representation coefficients on the data manifold. Further, beyond smoothness, it is suggested by recent theoretical work that we should ensure second order smoothness for the NMF mapping, which measures the linearity of the NMF mapping along the data manifold. Based on the equivalence between the gradient field of a linear function and a parallel vector field, we propose to find the NMF mapping which minimizes the approximation error, and simultaneously requires its gradient field to be as parallel as possible. The continuous objective function on the manifold can be discretized and optimized under the general NMF framework. Extensive experimental results suggest that the proposed parallel field regularized NMF provides a better data representation and achieves higher accuracy in image clustering.

The necessary step in the diagnosis of leukemia by the attending physician is to classify the white blood cells in the bone marrow, which requires the attending physician to have a wealth of clinical experience. Now the deep learning is very suitable for the study of image recognition classification, and the effect is not good enough to directly use some famous convolution neural network (CNN) models, such as AlexNet model, GoogleNet model, and VGGFace model. In this paper, we construct a new CNN model called WBCNet model that can fully extract features of the microscopic white blood cell image by combining batch normalization algorithm, residual convolution architecture, and improved activation function. WBCNet model has 33 layers of network architecture, whose speed has greatly been improved compared with the traditional CNN model in training period, and it can quickly identify the category of white blood cell images. The accuracy rate is 77.65% for Top-1 and 98.65% for Top-5 on the training set, while 83% for Top-1 on the test set. This study can help doctors diagnose leukemia, and reduce misdiagnosis rate.

Model classification is essential to the management and reuse of 3D CAD models. Manual model classification is laborious and error prone. At the same time, the automatic classification methods are scarce due to the intrinsic complexity of 3D CAD models. In this paper, we propose an automatic 3D CAD model classification approach based on deep neural networks. According to prior knowledge of the CAD domain, features are selected and extracted from 3D CAD models first, and then preprocessed as high dimensional input vectors for category recognition. By analogy with the thinking process of engineers, a deep neural network classifier for 3D CAD models is constructed with the aid of deep learning techniques. To obtain an optimal solution, multiple strategies are appropriately chosen and applied in the training phase, which makes our classifier achieve better performance. We demonstrate the efficiency and effectiveness of our approach through experiments on 3D CAD model datasets.