Search Results (5,080)

Significant nonlinear radiation differences and weak texture differences exist between multimodal weak texture remote sensing images (MWTRSIs). When using traditional methods to match MWTRSIs, the low distinguishability of descriptors in weak texture regions results in poor matching performance. A robust matching method is proposed based on normalized structural feature transform (NSFT), which can extract spatial structural features of images while mitigating nonlinear radiation differences between weak texture regions. First, the bilateral filter is used to transform the weak texture remote sensing image into a normalized image, which not only greatly weakens the nonlinear radiation difference but also retains most of the structural information. Then, the UC-KAZE detector is designed to extract many evenly distributed feature points on the normalized image. Subsequently, a multimodal weak texture feature descriptor with rotation invariance is designed based on the self-similarity of the weak texture image. Finally, the initial correspondences are constructed by bilateral matching, and the mismatches are removed by the fast sample consensus (FSC) algorithm. We perform comparison experiments on eight types of MWTRSIs. The results show that the proposed method has good scale and rotation invariance and good resistance to nonlinear radiation differences and weak texture differences. Full article

Protein-based nanocarriers have gained considerable attention for targeted cancer theranostic applications due to their inherent biocompatibility, biodegradability, and facile functionalisation. In addition, some of their properties, such as self-assembling nature, low immunogenicity (if species matched), molecular recognition ability, and lack of persistence due to degradation into proteinogenic amino acids, make them highly suitable for oncology-related applications. Each protein-based nanocarrier exhibits unique physicochemical and biological properties. In this review, we summarise recent advances in targeted protein-based nanocarriers, including albumin, lipoproteins, ferritin, viral protein capsids, fibrin type proteins and silk proteins, emphasising receptor-specific targeting mechanisms, the integration of various imaging modalities along with their advantages and limitations, and the importance of employing advanced preclinical models for translational theranostic applications. This review also discusses the most recent and significant studies in the field, providing useful insights into future directions of protein-based nanocarriers for cancer theranostics. Full article

The upper atmospheric microwave sounding channels data are important for atmospheric data assimilation and retrieval. However, radiative transfer simulation accuracy is constrained by the precise characterization of the Zeeman splitting effect. This study investigates key influencing factors in upper-atmospheric microwave radiance simulations, focusing on the geomagnetic field parameters and the Zeeman splitting absorption coefficients. A three-dimensional (3D) atmosphere-magnetic coupling dataset is constructed using the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) version 2.0 Level 2A atmospheric profiles and the International Geomagnetic Reference Field (IGRF-13) as input for the microwave Line-by-Line (LBL) model. Observations from Special Sensor Microwave Imager/Sounder (SSMIS) channels 19 and 20 are used to quantitatively compare the effects of 2D and 3D geomagnetic fields on simulations and evaluate the impact of updated Zeeman splitting coefficients. Quantitative analysis reveals that the average vertical attenuation rate of geomagnetic field strength between 50 and 0.001 hPa is 2.98%, and using 3D magnetic field parameters improves the observation and simulation bias (O-B) for SSMIS channels 19 and 20 by approximately 3.67% and 3.52%, respectively. The updated microwave LBL model, incorporating molecular self-spin interactions and higher-order Zeeman effects, reduces the mean absolute error (MAE) and root mean square error (RMSE) of the SSMIS channel 20 by approximately 2.7% and 2.25%, respectively. Experimental results indicate that the 7⁺ line within a 2 MHz frequency shift is sensitive to moderate magnetic field strength (0.35–0.55 Gauss), while the 1⁻ line is sensitive to strong magnetic fields (0.5–0.7 Gauss). This study demonstrates that optimizing geomagnetic field representation and Zeeman splitting coefficients can improve upper atmospheric microwave radiance simulation accuracy by detailed comparison with observations. Full article

Background/Objectives: Sociocultural attitudes toward appearance and body image are important components of women’s psychological well-being, particularly in the context of physical activities involving body exposure, such as pole dance. The aim of this cross-sectional study was to compare body image and sociocultural attitudes toward appearance between women practicing pole dance and women not engaged in this activity, and to examine the associations between these variables. Methods: The study included 207 women practicing pole dance (PDG) in clubs and schools across Poland and 180 women not practicing this discipline, who served as the control group (CG). Data were collected using the CAWI (Computer-Assisted Web Interview) method with a proprietary questionnaire and standardized tools: the Sociocultural Attitudes Towards Appearance Questionnaire 3 (SATAQ 3) and the Body Esteem Scale (BES). Results: Women practicing pole dance had lower mean BMI and were less frequently overweight but more frequently underweight compared to the control group. They obtained significantly higher scores on the Internalization–Pressure and Internalization–Athlete scales of the SATAQ 3. Significant between-group differences in body image were observed only for the Physical Condition subscale of the BES, with higher scores in the pole dance group. Significant negative correlations were identified between sociocultural attitudes toward appearance and body image in both groups, with stronger associations observed among women practicing pole dance. Conclusions: Participation in pole dance was associated with higher self-evaluation of physical condition as well as stronger internalization of sociocultural appearance norms. Due to the cross-sectional design, the findings indicate associations rather than causal relationships. The results underline the importance of preventive and educational strategies promoting a functional rather than exclusively esthetic approach to the body. Full article

Knowledge distillation serves as a prevalent model compression strategy within scene text detection, enabling the transfer of learned representations from a high-capacity teacher architecture to a streamlined student counterpart. Building upon this concept, deep mutual learning alleviates dependence on the teacher model through interactive learning among student models. However, existing deep mutual learning networks inadequately address the complex redundant backgrounds and text feature distributions in scene text images, failing to effectively balance the trade-off between model performance and lightweight design. To address this issue, this paper proposes an improved self-mutual learning framework based on deep mutual learning. By employing a design that incorporates parallel multi-detection heads and interactive learning, the proposed approach simplifies the model training process while significantly improving detection accuracy. Specifically, the framework introduces a pruning mechanism that enables different detection heads to capture input features with varying degrees of sparsity. This not only reduces interference from redundant backgrounds but also leads to a more lightweight implementation. Moreover, varying feature sparsity among detection heads promotes more diverse knowledge exchange throughout mutual learning. This substantially boosts the distilled model’s resilience in intricate text environments. Comprehensive evaluations show that our approach achieves superior F-measure scores compared to leading knowledge distillation methods. Full article

Dental conditions, including caries, periodontal disease, plaque accumulation, malocclusion, and oral mucosal abnormalities, remain highly prevalent worldwide. Early detection is crucial for preventing disease progression, simplifying treatment, and improving patient outcomes. Conventional diagnostic methods rely on subjective visual and tactile examinations, which are often inconsistent. Recent advances in deep learning (DL), particularly convolutional neural networks and vision transformers, enable automated, accurate detection of dental diseases from intraoral images captured via smartphones or dedicated imaging devices. DL-driven systems facilitate cost-effective virtual consultations, community screenings, and remote oral health monitoring. This narrative review was conducted following a structured search of PubMed, Scopus, Web of Science, Embase, and Google Scholar (October 2020–October 2025), which identified 74 eligible studies on intraoral photographic imaging-based DL systems, encompassing caries, gingival inflammation, plaque, malocclusion, and soft-tissue lesions. Most studies focused on caries, plaque, and periodontal disease using CNN and U-Net-based models, often reporting accuracies above 85% but with substantial performance drops in external validation. Despite promising results, clinical integration remains limited by challenges such as class imbalance, limited external validation, heterogeneous imaging protocols, and insufficient model interpretability. Emerging approaches, including self-supervised and federated learning, explainable artificial intelligence, multimodal data fusion, and smartphone-based diagnostics, offer potential solutions. Standardized imaging workflows, high-quality annotations, and robust clinical trials are essential to translate DL-based dental diagnostic systems into real-world practice. This narrative review aims to guide the development of reliable, equitable, and clinically deployable DL solutions for oral health assessment. Full article

Scanning electron microscopes (SEMs) are crucial for material characterization. They are highly susceptible to vibration from environmental sources, internal components, and other external factors, which can impair measurement accuracy. Traditional solutions are limited in addressing multi-source vibrations: passive isolation struggles with internal vibrations, while image post-processing cannot fundamentally correct large-amplitude deviations in the electron beam. Therefore, this study proposes a hybrid framework that combines real-time active hardware suppression with post-processing to mitigate vibration-induced distortion in SEM images. Using a self-developed external controller and software, the framework extracts periodic vibration features via FFT, quantifies scan line horizontal offset, and implements real-time inverse offset during imaging to suppress dominant-frequency vibration at the source. An adaptive median filtering algorithm is integrated with a Laplacian edge enhancement algorithm to address residual edge burrs, thereby balancing distortion suppression and detail preservation. Experiments at 100 kx magnifications demonstrate notable correction effects: the peak-to-peak value, edge transition width (ETW), and no-reference image quality (NIQE) score are reduced by 39.4%, 91.7%, and 58.9%, respectively. Consistent correction trends are observed at 50 kx, with periodic vibration distortion essentially eliminated across both magnifications. Furthermore, distortion can be regulated through the phase interaction between dwell time and vibration period, making the strategy universally applicable and easy to implement. Without the need for vibration source localization, the framework is compatible with various types of vibration interference. It provides a solution for mitigating vibration impacts in high-magnification, precise characterization of SEMs and offers a reference for anti-vibration optimization of other microscopic techniques, such as transmission electron microscopy (TEM) and atomic force microscopy (AFM). Full article

Aimed at the problems of severe layover, interferometric phase aliasing and phase jumps caused by dense urban features, which lead to difficulties in phase unwrapping and insufficient automation and intelligence in building areas under complex scenes, this paper proposes a phase reconstruction and unwrapping method for interferometric synthetic aperture radar (InSAR) building layover areas in complex scenarios integrated with YOLOv11. Based on a self-constructed dedicated dataset, the YOLOv11 object detection network is trained to identify and locate building layover areas in synthetic aperture radar (SAR) images and extract their original interferometric phases. On this basis, by integrating the building facade interferometry model and the interferometric phase gradient model, regions dominated by facade scattering are effectively identified, and their interferometric phases are reconstructed to reduce scattering interference from non-relevant areas. Finally, the reconstructed phase is unwrapped using a quality-guided phase unwrapping method. Experimental results demonstrate that the proposed method can automatically and intelligently achieve phase unwrapping in building areas under complex scenes, providing reliable technical support for urban deformation monitoring and 3D reconstruction. Full article

Accurate monitoring of soybean seedlings in the field is a core component for implementing scientific management during the seedling stage and unlocking yield potential. Traditional manual survey methods are inefficient and highly subjective, making them inadequate for real-time assessment at the field scale. This study addresses challenges such as the small size of individual seedlings, dense inter-plant overlap, blurred boundaries, and complex interferences from soil residue and varying illumination by proposing a high-precision method for soybean seedling instance segmentation and georeferenced localization based on low-altitude (12 m) Unmanned Aerial Vehicle (UAV) imagery. By implementing targeted improvements in the YOLOv11n-seg model, we developed the YOLOv11-seg-SSC model, which integrates the SCSA (Shared Cross-Semantic Space and Progressive Channel Self-Attention) mechanism, the Context-Guided (CG) Block, and a lightweight Slim-Neck structure based on GSConv and VoV-GSCSP. While significantly reducing computational complexity (approximately 9.5 GFLOPs and 2.96 M parameters), the model improved the mean average precision for segmentation (mAP@0.5 Mask) from the baseline of 80.6% to 83.3%, maintained a stable detection mAP@0.5 (Box) at 95.9%, and achieved an overall segmentation precision of 85.1% and recall of 80.3%. This approach not only meets the requirements for near-real-time field processing but also outputs seedling spatial distribution results with true geographic coordinates through georeferenced mapping, thereby providing directly applicable data support for seedling count statistics, missing seedling diagnosis, population spatial pattern analysis, and variable-rate management. This study establishes a complete technical pipeline from precise UAV image segmentation to spatially informed seedling status decision support, offering a theoretical foundation for efficient and accurate monitoring of soybean seedlings in the context of smart agriculture. Full article

Chest X-ray (CXR) imaging remains the most widely used radiological modality for assessing pulmonary and cardiothoracic disease, yet its interpretation is inherently constrained by tissue superposition, subtle radiographic findings and marked inter-observer variability. Recent advances in artificial intelligence (AI) have driven significant progress in automated CXR analysis, supported by large public datasets, evolving annotation strategies and increasingly expressive deep learning architectures. This review presents a comprehensive synthesis of approaches for pulmonary abnormality detection, encompassing convolutional neural networks, transformers, multimodal and vision–language models and self-supervised representation learning. We critically discuss their strengths, limitations and vulnerability to label noise, domain shift and shortcut learning. In parallel, we examine dataset properties, annotation practices, robustness challenges, explainability methods and the heterogeneity of evaluation protocols that hinder fair comparison and clinical translation. Building on these observations, the review identifies key future directions, including foundation models, multimodal integration, federated and domain-generalized training, longitudinal modeling, synthetic data generation and standardized clinical evaluation frameworks. By integrating methodological and clinical perspectives, this work offers an up-to-date reference for researchers and clinicians and outlines a roadmap toward reliable, interpretable and clinically deployable AI systems for chest radiography. Full article

Background/Objectives: Body-esteem during adolescence is associated with depression, potentially through self-esteem, a key indicator of global self-worth. However, evidence regarding whether this mediating pathway differs by weight status remains limited. This study examined the mediating role of self-esteem in the relationship between body-esteem and depression among normal-weight adolescents and those with overweight or obesity. Methods: This cross-sectional secondary analysis utilized data from 1168 nationally representative 14-year-old adolescents who participated in the 15th wave of the Panel Study on Korean Children (2022). Data were collected between July and December 2022 through home visits conducted by trained interviewers. Mediation analysis was conducted using PROCESS Macro Model 4, adjusting for sociodemographic and psychosocial covariates. Results: Based on BMI classification, 77.7% of participants were normal weight and 22.3% were overweight or obese. Body-esteem was higher in normal-weight adolescents than in those with overweight or obesity. In both groups, body-esteem was positively associated with self-esteem and negatively associated with depression. After covariate adjustment, self-esteem partially mediated the association between body-esteem and depressive symptoms in normal-weight adolescents and fully mediated this association in adolescents with overweight or obesity. Conclusions: The psychological pathways linking body-esteem and depression differed by weight status. Self-esteem mediated this association in both groups, with a stronger mediating role identified among adolescents with overweight or obesity. These findings highlight the importance of considering weight status when examining psychological pathways related to body perception and emotional well-being. Full article

Since deep learning has been applied to medical image analysis, convolutional neural networks (CNNs) and Vision Transformers (ViTs) have become core architectures for medical image classification tasks. However, CNNs are constrained by local receptive fields and cannot effectively model long-range context-dependent correlations in medical images; ViTs face deployment challenges in high-resolution medical image tasks due to the quadratic computational complexity of self-attention. While State Space Models (SSMs) represented by Mamba offer a new solution with linear complexity, they suffer from redundant directional modeling and low parameter efficiency in direct medical image applications. This study proposes the efficient state space enhancement framework (MedMamba-ESS), integrating the SS2D-Top2 adaptive directional scanning mechanism (reducing SSM submodule FLOPs by ~37%) and the G-SSM grouped parameter sharing module (achieving 3–4% parameter compression and 4.4% accuracy improvement via regularization). Validated on 14 public datasets (9 imaging modalities, 9 anatomical regions, >240,000 images), MedMamba-ESS achieves superior performance at 2.0G FLOPs: its Overall Accuracy (OA) is 3% higher than the baseline MedMamba-Tiny on non-MedMNIST datasets (3.13% higher than other mainstream models) and 3% higher on MedMNIST datasets (1% higher than others). Ablation experiments confirm that the two modules synergistically reduce parameters by 1.05% and boost accuracy by 4.6%. This study overcomes the technical limitations of traditional SSMs in medical imaging applications, achieving synergistic improvements in both model performance and computational efficiency. It provides an architecture optimization solution that combines practicality and generalizability for the implementation of SSMs in medical image analysis. Full article

During the harvesting and storage of wheat, various impurities are often mixed in, which adversely affect the processing quality and food safety of wheat. Therefore, developing an efficient and accurate impurity detection method is of great importance. Terahertz (THz) imaging technology can acquire time-domain spectral transmission images of wheat impurities, providing more features and facilitating detection. However, due to the limitations of THz imaging system hardware and environmental factors, the acquired THz images are often contaminated with noise, resulting in blurred details and indistinct edges, which severely hinder the accurate identification of impurities. To improve the quality of THz images of wheat impurities, this study proposes an Edge-Enhanced and Feature-Fused Image Denoising Network (EEFDNet). The proposed network employs a dual-branch architecture: a denoising branch utilizing dilated convolutions to strengthen feature representation, and an edge enhancement branch designed to emphasize impurity contour information. The outputs of the two branches are integrated through a feature fusion module to effectively remove noise while preserving and enhancing structural details. Experimental results on a self-established THz image dataset of wheat impurities demonstrate that EEFDNet exhibits superior performance, with the PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) reaching 32.59 dB and 0.9180, respectively, outperforming several mainstream denoising models. Moreover, the proposed method exhibits strong robustness under high-noise conditions. This study provides an effective image preprocessing approach for wheat impurity detection and establishes a solid foundation for subsequent high-precision impurity identification. Full article

Four-dimensional (4D) flow MRI enables the comprehensive assessment of cardiovascular hemodynamics. To compensate for respiratory motion, self-gating strategies are typically used and perform reliably at clinical field strengths. With the recent push towards field strengths below 1 Tesla, these strategies need to be re-evaluated given the reduced signal-to-noise ratio (SNR). Camera-based, contactless respiratory monitoring offers an attractive alternative to self-gating, as it is unaffected by imaging. This study compared respiratory self-gating (SG) and camera-based (VE) binning for phase-contrast gradient-echo (PC-GRE) 4D flow MRI at 0.6 T. Data were acquired from twenty healthy subjects (age: 32.8 ± 12.6 years) using a pseudo-spiral undersampled Cartesian four-point velocity encoding scheme. Reconstructions were performed with FlowMRI-Net for the end-expiratory state using either SG or VE binning. SG and VE showed strong agreement, with cross-correlation coefficients of ~0.87, accuracies of ~0.87, and F1-scores of ~0.9. Velocity analysis revealed high concordance (R² = 0.99; RMSE = 3.9 cm/s), with mean differences in peak velocity of 1.25 ± 2.36 cm/s. In this feasibility study, respiratory self-gating and camera-based binning yielded similar hemodynamic parameters from PC-GRE 4D flow MRI at 0.6 T, with the camera-based approach being independent of MR image SNR. Full article

Acute respiratory distress syndrome (ARDS) is characterized by severe hypoxemia, low lung compliance, and marked regional heterogeneity of aeration, making the lung highly vulnerable to injurious mechanical forces. Mechanical ventilation is essential to maintain gas exchange. However, excessive stress and strain may contribute to ventilator-induced lung injury (VILI). The progressive transition to partial ventilatory support introduces an additional risk: patient self-inflicted lung injury (P-SILI), driven by vigorous inspiratory efforts, large transpulmonary pressure swings, pendelluft, and heterogeneous regional strain. Advances in monitoring, imaging, and physiology-based management offer the potential to reduce lung injury and improve outcomes in mechanically ventilated patients with ARDS. This review aims to summarize the clinical–physiological background of VILI and P-SILI, describe protective strategies during controlled and partially assisted ventilation, and discuss monitoring tools to personalize mechanical ventilation in ARDS. Full article