2026 September - 64 articles
Ovarian cancer (OC) is the second most common gynecological malignancy and remains one of the leading causes of gynecological cancer-related mortality worldwide. A major clinical challenge is the lack of an accurate and widely applicable strategy for...
Automated CT lung nodule classification suffers from domain shift across centers. We propose LoRA-GRL, combining Low-Rank Adaptation (LoRA) with adversarial domain harmonization via a Gradient Reversal Layer. Rank-8 LoRA modules were inserted into al...
Many existing deep-learning models for brain-tumor diagnosis report very high accuracy, yet they often suffer from overfitting, limited generalizability, and poor robustness, which reduces their clinical reliability. First, a large and diverse datase...
The facial muscles are crucial for nonverbal communication, physiological functions, and articulation. Unlike skeletal or masticatory muscles, they lack fascia, attach directly to the skin and function without joint-mediated movements or visual feedb...
Identity verification is important for safety management at industrial access points. Gait recognition provides contactless identity cues as workers pass through a gate, but public datasets rarely combine card-swiping, moving barriers, standard workw...
Object detection models can experience substantial performance degradation when the input representation at deployment differs from that used during fine-tuning. In this study, we investigate a specific grayscale-to-RGB distribution shift in which de...
Image precompensation aimed at correcting refractive errors of the human eye seeks to transform a displayed image so that, after being blurred by the observer’s ocular optics, the image projection on the retina closely matches the original pict...
Accurate differentiation of benign and malignant lung nodules in computed tomography (CT) is important for early lung cancer diagnosis and reliable clinical decision-making. Many existing deep learning methods emphasize either nodule-centred morpholo...
Common-path optical coherence tomography (CP-OCT) has garnered significant interest for use in biomedical imaging due to its compact configuration, enhanced phase stability, and reduced sensitivity to environmental disturbances. The imaging performan...
Remote sensing referring image segmentation faces critical challenges including arbitrary target rotation, drastic scale variation, cluttered complex backgrounds, and large visual-language semantic gaps. Existing mainstream segmentation models adopt...
This paper presents a stage-complete programmable-logic architecture for pairwise monocular structure from motion using calibrated, pre-undistorted 1920 × 1080 video. It integrates streaming feature extraction, Block-RAM-backed Top-K selection,...
Modern building infrastructures are becoming increasingly complex, creating a need for intuitive indoor navigation systems that can assist users in unfamiliar environments. Augmented Reality (AR) has emerged as a promising solution by providing spati...
Tiny-object detection remains challenging because repeated downsampling degrades the limited spatial information available for small targets. This paper proposes RIF-YOLO-N, a lightweight detector that strengthens the finest P3 pathway of YOLOv8n thr...
Industrial low-altitude multi-object tracking is challenged by overhead viewpoints, dense small targets, mutual occlusion, homogeneous appearances, and domain-shifted backgrounds. Vision-language pre-trained models provide useful semantic priors, but...
The visual whiteness of sugar is a key factor in determining its quality and purity. Consequently, the development of rapid and cost-effective methods for the color evaluation of sugar samples is of critical importance to the sugar industry. This stu...
Although Transformer-based real-time object detectors have achieved promising performance in autonomous driving scenarios, their ability to detect small objects remains limited. This limitation primarily arises because small objects occupy only a few...
Multimodal studies of prostate cancer typically report aggregate fusion gains from histology and transcriptomics but rarely characterize when each modality is informative. We asked whether morphology and transcriptomics contribute differently to dist...
Hearing loss represents a global challenge, affecting the ability of the deaf community to communicate, study, and work. Currently, the World Health Organization reports that more than 430 million people live with disabling hearing loss. Among rehabi...
Nutrient persistence, which manifests as residual deposits within parenteral and enteral delivery systems and as prolonged intraluminal retention in the gastrointestinal (GI) tract, represents a central determinant of safety and performance in clinic...
Task-based image quality assessment—the modulation transfer function, the noise power spectrum (NPS), the noise-equivalent quanta and model observers—fails by returning a plausible wrong number rather than an error, and the regression tes...
The Internet of Things has increasingly enabled advancing real-time environmental monitoring through the integration of Low-Power Wide-Area Networks. Among these technologies, LoRa (Long Range) has emerged as a prominent communication technology due...
Atrial fibrillation (AF) is associated with abnormal left atrial (LA) blood flow patterns that contribute to thromboembolic risk. While blood flow stasis in AF has been extensively studied, the effects of AF and pulmonary vein isolation (PVI) on LA v...
Multi-object tracking (MOT) in low-light environments presents significant real-world application value. Despite substantial progress in MOT, low-light MOT remains constrained by the scarcity of specialized datasets, largely because collecting and ma...
Reflections caused by poor lighting conditions or the presence of transparent media such as glass often degrade image quality by introducing unwanted visual artifacts and obscuring important scene information. These reflections not only reduce the pe...
Access to reliable medical images is essential for clinical training. To address this need, this paper presents an extended version of MIRAGE, a multimodal retrieval and generation system that utilizes a shared latent space to process medical queries...
Compact deep models are attractive for medical image classification, but conventional knowledge distillation mainly transfers class-level predictions and may not adequately preserve lesion-relevant spatial cues. To address this limitation, we propose...
Deep neural networks have achieved strong performance in medical image classification, but their deployment may be constrained by the computational cost of high-capacity models. Knowledge distillation (KD) addresses this problem by transferring knowl...
Semantic segmentation of architectural elements in cultural heritage sites lies at the intersection of computer vision and digital preservation. Moroccan historical monuments spanning mosques, madrasas, royal gates (babs), and mausoleums across Fez,...
Maritime object detection is essential for port-based surveillance, ship tracking, and maritime security. However, practical implementation encounters two primary challenges: significant class imbalance (IMO identification numbers represent only 1.2%...
Focus-measure recommendations for smear microscopy are typically established on one specimen type under one reference definition and one scoring rule, leaving their transferability untested. We benchmark 32 handcrafted focus measures on 26,100 z-stac...
Fog severely degrades the visibility of small unmanned aerial vehicles (UAVs) in long-range imagery, reducing the reliability of downstream detection and tracking. This paper presents a task-driven evaluation framework that links depth-aware syntheti...
Early and accurate cancer detection from medical imaging remains challenging because clinically relevant evidence is distributed across local image appearance, structural relationships between suspicious regions, and ordered imaging context. This stu...
This paper presents a novel end-to-end (E2E) empirical Bayes framework for learning the posterior distribution in linear inverse problems. The proposed framework models the log-posterior as the sum of a log-likelihood derived from the known forward m...
Continuous objective assessment of pig motor condition, including recovery after anesthesia, requires the joint analysis of posture, locomotion, transitions between states, and short-term adverse events. This study aimed to develop and algorithmicall...
Reflectance Transformation Imaging (RTI) has long been employed across multiple research domains to document surface textures, inscriptions, and fine morphological features, including paleontology. However, RTI requires specialized equipment and cont...
The rapid spread of deepfake content poses a significant threat to the credibility of digital multimedia, creating an urgent need for accurate and robust detection methods. This paper proposes a hybrid deep learning framework that combines EfficientN...
Human hands play a central role in manipulation, communication, and physical interaction, making their accurate digital reconstruction a long-standing challenge in computer vision and graphics. Reliable modeling of hand pose, hand shape, and interact...
Deep learning for Alzheimer’s disease (AD) detection from structural magnetic resonance imaging (MRI) needs large, labelled datasets, yet many cohorts hold only a few hundred participants, for which conventional augmentation adds little anatomi...
The tympanic membrane (TM) is a thin, semi-transparent structure whose morphology and optical appearance provide important diagnostic cues. In the early stages of middle-ear pathology, subtle shape and compliance alterations may precede overt clinica...
Alzheimer’s disease (AD) is characterized by extracellular β-amyloid (Aβ) plaques and intracellular hyperphosphorylated tau neurofibrillary tangles, with tau pathology correlating more strongly than amyloid pathology with cognitive de...
This paper proposes Phase-PCANet, a new image representation method for fingerprint liveness detection. Phase-PCANet integrates both local and global phase features. The local phase, capturing fine-grained edges and textures, is extracted via short-t...
Neurological disorders are a major cause of disability and mortality worldwide, and accessible methods for detecting tremor-related trajectory deviations remain an important research challenge. We present an augmented reality (AR)-based approach for...
Graph convolutional networks (GCNs) have become a mainstream approach for skeleton-based action recognition by modeling spatial dependencies among human joints. However, existing methods mostly rely on learnable adjacency matrices or average relation...
This PRISMA 2020 systematic literature review analyzes efficient transformer-based video object detection (VOD). Across five databases through August 2025, 1343 records were identified, with 16 studies meeting the final inclusion criteria after full-...
Background: Two-stage coarse-to-fine architectures are commonly used in medical image synthesis and refinement. However, when the distributional gap between coarse inputs and high-resolution targets is large, the second-stage generator may produce un...
This survey presents a structured review of deep learning-based techniques for image data hiding, proposing a three-paradigm taxonomy organized by the method’s operational relationship to the carrier image. We classify existing methods into mod...
Automated segmentation of the left and right ventricles (LVs and RVs) in cine cardiac MRI (CMR) underpins reliable volumetry and mass estimation. However, papillary muscles and trabeculae (PM/T) introduce clinically meaningful variability and exacerb...
Early and accurate detection of liver masses is essential for effective clinical management and improved patient outcomes, as treatment strategies differ significantly between benign and malignant lesions. Dynamic contrast-enhanced magnetic resonance...
Artifacts, including halos and saturated bright spots, are common in spaceborne optical images and can degrade the reliability of star extraction, space object detection, and photometric analysis. Traditional signal-processing methods, such as morpho...
Small-scale pedestrians in UAV imagery often exhibit limited pixel coverage, weak texture, and severe background interference, while progressive network downsampling can further degrade their short-side structures and increase missed detections. To a...
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