Search Results (1,952)

Backgrounds: Carotid atherosclerotic plaques (CAPs) are a reliable marker of systemic atherosclerosis and a predictor of cardiovascular events. Despite advances in prevention, the prevalence of CAPs in high-income regions remains uncertain due to heterogeneity in imaging definitions, study designs, and populations. We strived to provide an updated meta-analysis of population-based studies conducted in Europe and North America between 2015 and 2025, estimating the prevalence of CAPs in general populations. Methods: Following the PRISMA 2020 guidelines, PubMed and Web of Science were searched for original studies. Eligible studies reported CAPs prevalence in adult general populations using ultrasonography, computed tomography angiography, and magnetic resonance imaging. Pooled prevalence was calculated using a random-effects meta-analysis of proportions, and heterogeneity was assessed using I² and τ² statistics. Subgroup and meta-regression analyses explored associations with age and comorbidities. Results: A total of 80 studies comprising 177,196 participants were included. The pooled prevalence of CAPs was 39.8% (95% CI 32.6–47.5%) under a random-effects model with substantial heterogeneity (I² = 99.6%). The prevalence of CAPs increased with age, exceeding 59% among individuals aged over 70 years. High-risk populations, particularly those with T2DM, exhibited a prevalence exceeding 50%. Conclusions: CAPs are present in approximately 40% of adults in Europe and North America, with prevalence strongly driven by age and comorbidities. Despite therapeutic advances, the prevalence of CAPs has not declined, reflecting the growing impact of population aging and comorbidities. Standardized imaging definitions, longitudinal outcome linkage, and pragmatic prevention strategies are needed to translate CAPs detection into reduced cardiovascular events. Full article

Background and Purpose: Tenecteplase (TNK) within 4.5 h from symptom onset is not inferior to alteplase in treating ischemic stroke. In recent years, some randomized controlled trials (RCTs) have investigated the efficacy of extending the therapeutic window up to 24 h. This updated meta-analysis aims to synthesize the results of these RCTs comparing TNK to the best medical treatment (BMT) with or without endovascular thrombectomy. Methods: In accordance with PRISMA guidelines, all RCTs comparing TNK with BMT in adult patients between 4.5 and 24 h were systematically searched. The primary endpoint was good functional outcome at 90 days (mRS 0–2). Secondary endpoints included excellent outcome (mRS 0–1), symptomatic intracerebral hemorrhage (sICH), 90-day mortality, complete reperfusion at 24 h. Odd and Hazard ratios (ORs; HRs) were pooled using meta-analytic methods. Results: A total of seven RCTs involving 1754 patients were included. The rates of the primary endpoint were higher in TNK-treated patients (HR: 1.15; 95% CI: 1.03–1.27), as were rates of excellent functional outcome (HR: 1.29; 95% CI: 1.08–1.55). In the subgroup receiving intravenous therapy (IVT) alone, the primary endpoint was significantly more frequent in the TNK group than in the BMT group (OR: 1.47; 95% CI: 1.17–1.84; p for heterogeneity < 0.0001). TNK treatment was also associated with higher reperfusion rates compared with BMT, reflecting a greater proportion of saved ischemic penumbra as assessed via perfusion imaging. Although symptomatic intracranial hemorrhage (sICH) occurred more frequently in TNK-treated patients, the difference did not reach statistical significance, and mortality rates were comparable between treatment groups. Conclusions: Tenecteplase administered between 4.5 and 24 h is associated with improved rates of both good and excellent functional outcomes compared with BMT, especially in patients treated with IVT alone. Additionally, TNK is linked to higher rates of reperfusion. Full article

Robotic sorting-and-filling tasks in flexible manufacturing require robots to reproduce specified in-box arrangements while adapting to variations in container poses, object availability, sensing conditions, and external interventions. This paper proposes a box-centric one-shot teaching framework for robotic packing tasks with relative pose constraints. In the teaching stage, a human operator demonstrates the desired packing layout only once. The system uses reference-prompted SAM-based contour refinement to extract box and in-box object contours, object categories, quantities, and relative position and orientation constraints. These constraints are then converted from pixel-plane measurements into box-local pose constraints, forming a reusable box-centric packing template that preserves both translational and angular layout information. During execution, the recorded template is transferred to detected box instances with different global poses, and executable pick-and-place commands are generated through a task-level perception-to-command pipeline. A mechanism for continuous assignment and state updates is further introduced to maintain residual target slots, update object-to-slot allocation, and report missing or redundant objects across execution rounds. Single-box template transfer experiments achieved mean placement errors of 7.16 mm and 7.57 mm for two recorded templates, while representative post-execution images further showed that the relative object orientations were visually preserved with respect to the taught template footprints. Multi-box experiments demonstrated that unfinished residual slots could be preserved and completed after scene updates without re-teaching. Additional validation with different container types and object shapes showed the feasibility of extending the framework beyond cube-only cases. Ablation tests under nine exposure settings further showed that SAM refinement improved template-acquisition robustness compared with the previous recognition method. These results verify that the proposed framework enables one-shot template acquisition, box-centric layout transfer, relative pose preservation, and persistent task-level execution for constrained robotic packing tasks. Full article

Optical imaging is among the safest and most highly impactful biomedical imaging modalities. Aberration in optical imaging systems leads to distorted images. This distortion is almost nonlinear and hence affects the relative size, intensity and appearance of image details. Image aberration has many types, with some or all of them able to be imposed on the image based on the quality of the imaging system and/or surrounding conditions. Many approaches have been introduced to remove or minimize aberration from optical images. If the transfer function of an imaging system and the function of the noise added during the imaging process are known, then an ideal image can be obtained from the image produced by this system. The point spread function (PSF) of an optical imaging system is the image it produces for a point object. PSF is the observable form of the transfer function. The transfer function itself is the exit pupil function or typically the system aberration. The nonlinearity and multiplicity of the aberration imposed on the image, together with the added noise, make it difficult to obtain the transfer function from the degraded images. In this work, optimization and global search techniques are utilized in an iterative image restoration algorithm to estimate the transfer function and restore the image. The proposed technique updates an initially suggested solution of transfer function by optimizing the aberration coefficients. The final solution of the transfer function and hence the PSF is reached when the optimum restored image is obtained. The proposed algorithm is validated by a set of 12 images degraded by different combinations of aberration patterns. Many statistical metrics are used to assess the performance of the proposed algorithm, resulting in a 100% success rate with SSIM equals 1 and a MAE range from 0 to $1\times {10}^{-8}$. Full article

The deep classifiers used for chest X-ray diagnosis can be sensitive to the visual frame around the evidence, producing correct labels for the wrong reasons by relying on the background, borders, text marks, or acquisition artifacts. This framing sensitivity reduces their trustworthiness, and they can fail under a distribution shift. Inspired by biological vision, especially figure–ground segregation and selective attention, we propose a bio-inspired adversarial attention alignment training process that encourages evidence-centered decisions without changing the classifier structure. A classifier is first trained with image-level labels. Class activation mapping (CAM) is then used to produce a differentiable heatmap that indicates where the model attends. We treat this heatmap as a generated localization map and train a discriminator to distinguish the generated heatmaps from ground truth masks. The classifier is updated using a joint objective that preserves the classification performance while pushing its CAM heatmap toward a mask-like structure, reducing the reliance on background cues. We also introduce evaluation measures for the test phase, including augmentation inconsistency (prediction flip rate under angle-based augmentations) and framing sensitivity (CAM energy outside the mask). The experiments show improved lung-focused attention and robustness, while requiring masks only during training and no additional inputs at inference. Full article

Low-light image enhancement aims to restore visually pleasing normal-light images from degraded low-light observations. Most existing methods handle luminance variation from the enhancement perspective. As a result, the degradation process from a normal-light image to a low-light observation is usually not explicitly characterized. In addition, degradation-oriented optimization is often computationally expensive due to repeated iterative updates. To address these issues, based on the alternating direction method of multipliers (ADMM), a degradation-oriented step-size-consistent unfolding network SCAUNet is proposed. Specifically, a low-light image is modeled as the element-wise product of a normal-light image and a luminance degradation operator, together with additive noise. Based on this formulation, low-light enhancement is converted into the joint estimation of the target image and the degradation operator. Then, a state-based one-step ADMM solver is developed, and a step-size consistency constraint is introduced to improve the reliability of one-step unfolding. Extensive experiments on LOL-v1 and LOL-v2 demonstrate the effectiveness of the proposed SCAUNet. Compared with existing state-of-the-art methods, SCAUNet yields better enhancement quality, especially in preserving image structures, correcting illumination, and suppressing artifacts. Strong generalization ability is also verified on four no-reference low-light datasets, and promising results are obtained on single-image exposure correction. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy. The identification and management of precursor lesions, particularly the increasingly common intraductal papillary mucinous neoplasms (IPMNs), pose a significant challenge, creating a profound clinical dilemma between intercepting pancreatic ductal adenocarcinoma and avoiding surgical overtreatment. This literature review aims to synthesize the latest evidence to facilitate a transition from purely morphology-based surveillance toward a biologically informed risk stratification paradigm. This approach could provide a personalized risk-stratification algorithm that optimizes therapeutic management and enables timely intervention for pancreatic cancer. By using PubMed, Embase, Scopus, and Web of Science, we analyzed and summarized key findings from recent literature (2020–2025), including cohort studies, mechanistic analyses, evidence-based guidelines, and systematic reviews on cyst fluid biomarkers (CEA panels, DNA/RNA sequencing), and emerging AI applications. Prospective and multicenter studies consistently report that NOD is independently associated with high-risk stigmata, cyst progression, and malignant transformation. Mechanistic research suggests a bidirectional interplay between the evolving neoplasia and pancreatic endocrine dysfunction. Updated guidelines underscore the need for more precise diagnostic algorithms. Recent work demonstrates that advanced cyst fluid markers—CEA panels, DNA/RNA sequencing, and multi-omic signatures—significantly improve diagnostic accuracy. Furthermore, explainable AI models show encouraging performance in predicting malignancy and assisting patient triage. Risk stratification in PCLs is shifting from morphology-based assessment toward integrated, multimodal approaches combining clinical, endocrine, imaging, molecular, and computational data. Recent evidence positions new-onset diabetes as a clinically accessible and biologically plausible marker of high-risk IPMNs. Similarly, molecular assays and AI-enhanced analytics provide an additional layer of diagnostic precision. The development of personalized risk prediction algorithms could improve early detection of malignancy while reducing unnecessary surgical resections. Full article

In this paper, we propose a novel Depth from Focus (DFF) framework that formulates depth estimation as an energy minimization problem and unrolls the corresponding iterative optimization into a trainable neural architecture. Given a focal stack, a deep feature extractor constructs a learned focus volume that encodes defocus and structural cues. Based on this representation, multiple candidate depth maps are generated using a plane-based probabilistic formulation, while an attention mechanism adaptively assigns pixel-wise confidence weights to each candidate. The depth estimation is performed through an iterative refinement process, where each stage corresponds to a learned proximal update implemented via lightweight conditional networks. These updates incorporate focus consistency, adaptive step sizes, and learned regularization priors, enabling effective integration of physical imaging constraints with data-driven modeling. A final refinement module further enhances prediction accuracy by fusing the refined depth, focus volume features, and candidate hypotheses to estimate residual corrections. The entire framework is trained end-to-end, ensuring coherent optimization across all components. Experimental results demonstrate that the proposed method achieves improved robustness and accuracy, particularly in low-texture and noisy regions, while preserving interpretability through its unfolding-based design. Full article

Referring Expression Comprehension (REC) localizes a target object in an image given a natural-language referring expression and is a core benchmark for fine-grained vision–language alignment. Recent detection-style multimodal Transformers achieve strong REC performance but typically rely on high-capacity visual and textual backbones, incurring substantial storage and compute costs. Replacing these backbones with lightweight alternatives greatly reduces model size, yet often degrades cross-modal alignment and yields a persistent accuracy gap. We propose CARM, a minimally invasive Cross-modal Alignment Recovery Module inserted between lightweight backbones and the downstream multimodal Transformer, requiring no changes to either component. CARM injects complementary priors via bidirectional prompts and uses a Cross-Attention Gate (CAG) to adaptively filter and scale prompt-induced updates; it further integrates Tree-of-Attributes Prompts (TAPs) to strengthen fine-grained grounding of attributes such as color, size, and spatial location. On RefCOCO, switching to lightweight backbones drops P@1 (IoU ≥ 0.5) to 84.51, while CARM improves it to 86.23, recovering most of the loss. Meanwhile, the overall model storage (checkpoint) is reduced by about 80%, demonstrating that the cross-modal alignment degradation induced by compression can be effectively restored. Full article

Plant disease recognition models trained under laboratory conditions often degrade markedly after cross-domain transfer because of the pronounced distribution gap between source and target domains and the scarcity of labeled target-domain samples. To address the transfer task from PlantVillage (PV_100) to PlantDoc, this study develops and evaluates a lightweight cross-domain few-shot plant disease recognition method under a strict PlantVillage-to-PlantDoc protocol. The method integrates EfficientNet-B0 feature extraction, cosine-similarity-based prototypical classification, and training-time target-domain BN adaptation (TBA). During training, unlabeled target-domain images are used only for BN statistical calibration, whereas inference is limited to feature extraction and prototype matching, without gradient updates or iterative optimization. Under a unified experimental protocol, the proposed method achieved cross-split mean accuracies of 42.69 ± 0.62% for one-shot and 54.24 ± 0.72% for five-shot, where ± denotes the standard deviation across three strict data splits; it outperformed ProtoNet by 7.44 and 9.43 percentage points, respectively. Ablation results indicate that TBA is the main source of performance improvement, whereas more complex adaptation strategies do not yield stable additional gains. The core encoder can be executed entirely on the NPU, with an estimated single-sample inference latency as low as 0.658 ms, indicating strong potential for encoder-level mobile deployment. Full article

Recovering high-quality images from low-quality speckle patterns remains a core challenge in scattering imaging, especially under narrowband illumination with ambient light interference and broadband illumination. This paper proposes a dual-input synchronous transmission architecture: after Correction-Smoothing-Phase Optimization (CSPO) preprocessing, two data streams are parallel-fed into Phase-Aligned Coherent Summation (PACS) for efficient and high-precision reconstruction with adaptive fusion, breaking the single-path limitation of traditional methods and balancing imaging efficiency and quality. Additionally, an adaptive enhancement factor feedback mechanism is designed for Median-Unsharp Sharpening Enhancement (MUSE) to dynamically adjust Median Filtering (MF) and Unsharp Masking (USM) parameters, achieving adaptive balance between noise suppression and detail enhancement and improving robustness under extreme lighting. In PACS, a dynamic reference update mechanism is introduced, combined with fixed amplitude to realize iterative phase optimization, effectively suppressing speckle noise and boosting the signal-to-noise ratio of reconstructed images. Experimental results show that the proposed method achieves favorable restoration performance even at a SNR of −8.7 dB under narrowband and broadband illumination with spectral bandwidths of 100 nm, 200 nm, and 280 nm (FWHM), and significantly improves image quality in unknown scattering media, showing great potential for robust speckle reconstruction. Full article

The solar corona has an extremely low density, and its brightness is only about one millionth of that of the photosphere. High-dynamic-range imaging of its faint structure is therefore essential for studying coronal heating, coronal mass ejections, and space weather. Quantitative coronagraph imaging requires flat-field measurement and calibration, which underpin intensity calibration, small-scale feature detection, and long-term cyclic analysis. This paper analyzes the coronagraph imaging chain (baffle–optical system–detector) and the origins of flat-field errors, including optical aberrations, stray light, and pixel-response non-uniformity, and summarizes the resulting calibration requirements of next-generation coronagraphs. On this basis, ground-based and space-based flat-fielding methods are systematically reviewed: the ground-based methods include integrating-sphere uniform light sources, opal glass/diffuser plates, clear-sky and thin-cloud backgrounds, and solar disk scanning, while the space-based methods include internal light sources and diffuser plates, attitude-roll and off-corona offset observations, and multi-phase statistical self-consistent flat-fielding. Their accuracy, resource cost, and applicability are compared. The review shows that no single method is simultaneously high-precision, easy to update, and engineer-friendly; a hierarchical, multi-method calibration framework is therefore recommended. Finally, a new method is proposed in which lithographically generated structured light fields, combined with Fourier optics and machine learning inversion, are used to estimate the pixel-response function. Preliminary experiments show that this method achieves a lower residual error than the integrating-sphere and opal glass methods, providing a high-precision reference for future wide-band, high-resolution coronagraph calibration. Full article

Zero-shot anomaly detection is attractive for industrial inspection, where target-domain training data are often unavailable for newly introduced products. Recent CLIP-based methods have demonstrated promising generalization, and online memory mechanisms can further improve adaptability by incorporating incoming test samples. However, unreliable or ambiguous evidence may be incorporated during online memory updates, which can degrade subsequent predictions, especially for weak or visually unstable categories. In this work, we propose TSMR, a lightweight extension of RareCLIP for online zero-shot anomaly detection. Rather than modifying the backbone or redesigning the anomaly scoring pipeline, TSMR improves the reliability of test-time memory evolution through a class-sensitive selective update strategy. Specifically, it combines a confidence quantile gate, a text-prior-based reliability check, and weak-class selective activation to derive a frame-level memory-update decision during online inference. Experiments on VisA and MVTec AD show that TSMR achieves clear improvements on VisA while maintaining competitive performance on MVTec AD. Under the online protocol, TSMR improves the reproduced RareCLIP baseline on VisA from 94.4% to 95.1% in image-level AUROC, from 98.8% to 98.9% in pixel-level AUROC, and from 93.5% to 94.0% in PRO. On MVTec AD, TSMR achieves 98.0% image-level AUROC, 97.6% pixel-level AUROC, and 93.6% PRO, remaining competitive with the strong RareCLIP baseline. Object-wise and seed-wise analyses further indicate that selective memory refinement is particularly beneficial for selected weak categories and remains stable across different online evaluation orders. These results suggest that reliable online memory evolution is an effective direction for CLIP-based zero-shot anomaly detection. Full article

Corrosion-driven section loss in steel tower structures erodes load-carrying capacity, yet field assessment still relies on subjective visual grading. This paper presents a closed-loop framework coupling quantitative image-based corrosion measurement with stochastic degradation modeling, Monte Carlo reliability simulation, and Sobol’ variance-based global sensitivity decomposition. Two complementary segmentation paths—hue–saturation–value (HSV) color-space thresholding for fleet-scale screening and DeepLabV3+ deep learning for detailed evaluation—convert imagery into calibrated section-loss estimates via nonlinear regression. Three analysis modes (single-image, multi-angle weighted-median fusion, and Oriented FAST and Rotated BRIEF (ORB) feature-matched temporal differencing) feed a Bayesian-updated power-law corrosion growth model whose outputs propagate through a time-dependent limit-state function via 10⁶-sample Monte Carlo simulation. Sobol’ indices rank each uncertain input’s contribution to the reliability-index variance. A field demonstration on a 40-year-old galvanized lattice tower in an ISO 9223 C4 coastal environment shows that the corrosion rate constant and zinc coating thickness together govern 65% of the total reliability variance and that a risk-ranked selective maintenance strategy reduces expected life-cycle cost by 71% relative to blanket intervention. Full article

Existing multi-modal UAV tracking methods typically rely on fixed-interval dynamic template update strategies to capture diverse target appearances, together with predefined thresholds to select high-quality search regions for template update. However, due to the irregular motion of targets and the complexity of real-world scenarios, such passive update mechanisms suffer from notable limitations. Fixed sampling intervals often fail to adequately capture appearance variations, while fixed threshold-based selection is insufficient to accommodate diverse imaging conditions, leading to ineffective updates or the introduction of noisy templates, thereby degrading tracking robustness and accuracy. To address these issues, we propose a search region-guided adaptive dynamic template update framework for robust multi-modal UAV tracking, aiming to improve both scene adaptability and target matching capability. Specifically, we design a Guided Template Selection Transformer, which dynamically matches templates conditioned on the current search region, enabling the tracker to autonomously select the most suitable template for the target’s current state. Furthermore, we introduce a Dynamic Threshold Module that adaptively adjusts template selection criteria according to different tracking scenarios, ensuring the reliability and contextual relevance of candidate templates. In addition, we develop a Dynamic Template Memory Module to maintain an ordered repository of target templates under different target states, providing a structured and high-quality template pool for the proposed selection mechanism. Extensive experiments on a standard multi-modal UAV tracking benchmark demonstrate that the proposed method significantly outperforms existing approaches, effectively overcoming the limitations of conventional fixed update strategies. Moreover, the proposed approach exhibits strong generalization capability across three additional multi-modal tracking datasets from typical surveillance scenarios. Full article