Search Results (26,683)

Background/Objectives: Peritoneal metastasis represents the most frequent and prognostically unfavorable metastatic pattern in gastric cancer, largely due to limited sensitivity of conventional imaging, delayed diagnosis, and insufficient response assessment. The aim of this review is to provide an overview of the current evidence on the diagnosis and treatment of gastric cancer with peritoneal metastases and to address current treatment limitations and options. Methods: This review was designed as a narrative review and is based on an extensive literature search in established databases. Results: Systemic chemotherapy remains the cornerstone of palliative treatment, improving the survival and quality of life compared with the best supportive care; however, outcomes in peritoneally metastatic disease remain poor. Advances in molecularly targeted and immune-based therapies have extended survival in selected patient populations, yet favorable molecular profiles are mainly unknown in peritoneal metastases. Staging laparoscopy and semi-quantitative assessment using the Peritoneal Cancer Index (PCI) are therefore essential for accurate diagnosis, prognostication, and treatment selection. Growing evidence from retrospective studies, multi-institutional cohorts, and selected randomized trials suggests that a multimodal approach—combining systemic therapy with intraperitoneal or bidirectional chemotherapy—may improve survival and quality of life. In carefully selected patients whose primary gastric tumor and peritoneal lesions respond to systemic treatment, complete cytoreductive surgery (CRS) followed by hyperthermic intraperitoneal chemotherapy (HIPEC) may further enhance outcomes and, in rare cases, achieve long-term survival. These potential benefits appear to be limited to highly selected patients with a low peritoneal tumor burden (PCI ≤ 6–7), positive cytology, good performance status, controlled extraperitoneal disease, and a high likelihood of achieving complete macroscopic cytoreduction (CC-0). Conclusions: Although the treatment intent in metastatic gastric cancer remains primarily palliative, carefully selected patients with limited peritoneal metastases may benefit from intensified multimodal treatment strategies when managed in specialized centers. Interdisciplinary evaluation, accurate staging, and individualized treatment planning are essential to optimize outcomes in this challenging disease setting. Full article

Hair loss, particularly androgenetic alopecia (AGA) and alopecia areata (AA), is a prevalent condition with widespread psychosocial impact. Recently, low-level laser therapy (LLLT) has emerged as a promising non-invasive therapeutic alternative due to its bioregulatory effects and favorable safety profile compared to conventional pharmacological treatments. In this study, we employed fluorescence lifetime imaging microscopy (FLIM) to investigate the effects of red-light irradiation on hair follicle dynamics and the cutaneous microenvironment in a C57BL/6 mouse model. A hair regeneration model was established to evaluate the efficacy of 650 nm red-light irradiation (bandwidth ± 20 nm). Then, the skin tissue was stained with hematoxylin and eosin (H&E) and followed by FLIM analysis to provide a multidimensional assessment of tissue morphology and metabolic status. Results showed that red-light irradiation significantly increased hair follicle numbers and enhanced adenosine triphosphate (ATP) levels in the skin tissue. FLIM analysis further revealed prolonged fluorescence lifetime values across different epidermal and dermal layers in the irradiated group, indicating significant alterations in the skin metabolic microenvironment. Furthermore, phasor plot analysis enabled precise differentiation between hair follicles and their surrounding skin structures, highlighting FLIM’s high sensitivity and accuracy in evaluating hair growth. In conclusion, this study has provided novel imaging-based insights into the mechanisms of LLLT-induced hair regeneration, highlighting the potential of FLIM as a powerful tool for characterizing the cutaneous microenvironment and quantitatively evaluating phototherapeutic efficacy in future translational applications. Full article

Epstein–Barr virus (EBV)-positive primary central nervous system lymphoma (PCNSL) is a rare entity typically associated with profound immunosuppression, most commonly in transplant recipients or individuals with HIV. We report a case of EBV-positive PCNSL arising in a 75-year-old male with myasthenia gravis receiving chronic mycophenolate mofetil (MMF) therapy outside the transplant setting. The patient presented with progressive neurological deficits, and brain magnetic resonance imaging demonstrated multiple enhancing lesions. Stereotactic biopsy revealed diffuse large B-cell lymphoma of non–germinal center subtype with immunoblastic features and EBV-encoded RNA (EBER) positivity, confirming EBV-positive PCNSL. MMF was discontinued, and the patient was treated with rituximab and high-dose methotrexate, resulting in stable disease. This case highlights that prolonged MMF therapy may confer sufficient immunosuppression to permit EBV-driven lymphoproliferative disease even in non-transplant patients. Early recognition, withdrawal of immunosuppression, and initiation of methotrexate-based chemotherapy can lead to favorable outcomes. Full article

Softmax is a step in transformer computation during which the internal buffer size grows rapidly because of the use of the exponential function. Softmax is a fundamental yet computationally expensive operation in vision transformer attention, posing significant challenges for deployment on resource-constrained FPGAs (Field Programmable Gate Arrays). Computational precision demands grow at the softmax stage in the attention pipeline mainly because of the use of the exponential function in the softmax computation. This paper proposes a low-precision softmax approximation that combines a truncated Maclaurin-series exponential with input-range clamping to enable efficient hardware realization without sacrificing reconstruction quality. By bounding extreme attention scores that contribute negligibly to final outputs, the proposed method mitigates the instability of low-order polynomial approximations while preserving their hardware efficiency. The approach is first validated in software using SwinIR (Image restoration using the SWIN Transformer) super resolution to ensure reconstruction fidelity and is then analyzed for FPGA deployment. SWINIR is a multi-stage version of other transformers like Deit and Vit, making it a preferred option for testing the reconstruction fidelity of the change for transformers. Experimental results demonstrate that the proposed fourth-order clamped approximation achieves near-reference performance, incurring only 0.15 dB PSNR and 0.0059 SSIM degradation on SwinIR-M, while significantly reducing precision and memory requirements. For the large-sized SWINIR model (SWINIR-L), a PSNR increase with a less than 0.01 SSIM loss is observed, further highlighting the insignificance of extreme values as model size gets bigger. A Horner-form reformulation further improves hardware efficiency by limiting intermediate precision growth. Overall, this work presents a reconstruction-aware and hardware-friendly softmax reformulation that enables practical deployment of vision transformers on small FPGA platforms. This work also uses this contribution to improve the performance of the ViTA accelerator design. We also add bias initialization and a PE loop bound runtime variable to the existing ViTA accelerator design. Full article

Background/Objectives: Cancer remains one of the leading causes of mortality worldwide, with lung and colon cancers among the most prevalent. Conventional histopathological diagnosis is time-consuming, requires expert pathologists, and is susceptible to human error. Methods: To address these limitations, this study proposes an automated classification framework for lung and colon cancer using histopathological images. The proposed method employs a lightweight pretrained deep learning model, MobileNetV3-Small, through transfer learning. Training is performed on an enhanced version of the LC25000 dataset, in which redundant image patches are removed to improve robustness and clinical generalizability. The images were initially available in multiple resolutions, which are resized to 224 × 224 × 3 to match the canonical input size of MobileNetV3-Small. Deep features are extracted from the dropout layer as it provides regularized representation of high-level features by reducing the overfitting (dimension N × 1024), which are optimized using a differential evolution algorithm, reducing the feature space to N × 60. These optimized features are evaluated using multiple classifiers. Results: Experimental results demonstrate a maximum classification accuracy of 98.14% using a Quadratic Support Vector Machine (SVM) and a 21.3× speed-up achieved with bagged trees, outperforming several state-of-the-art approaches representing a 3.34% improvement over the baseline study on the enhanced dataset. Conclusions: The results confirm that the proposed framework effectively balances high accuracy with computational efficiency. The use of a lightweight deep model combined with feature optimization makes the approach well-suited for practical clinical environments. Full article

The utilization of Artificial Intelligence-Generated Content (AIGC) has attracted widespread attention in video content creation. To generate high-quality videos, this paper presents a controllable multimodal fusion architecture for AIGC-driven short-video production. This architecture employs hierarchical constraint mechanisms and a multimodal attention fusion mechanism to enhance video content coherence and user controllability. Specifically, a scene coherence scheme is first designed to construct graph-based global and transition-level constraints by integrating text descriptions, reference images, and audio features. By leveraging the extracted style vector data, preliminary video clips are then generated through a combination of the cross-modal fusion unit and the spatio-temporal consistency unit. Finally, a fine-grained adjustment mechanism is implemented to ensure logical consistency and stylistic uniformity in the AIGC-generated videos. Experimental results indicate that the proposed architecture improves generation quality, controllability, and cross-segment coherence under the adopted evaluation settings. Full article

Accurate identification of plant numbers is crucial for evaluating the effectiveness of mechanical rice seedling transplanting, which directly affects yield estimation and replanting decisions in precision agriculture. Conventional manual counting methods are time-consuming and labor-intensive, which hinders their application in modern agriculture, where efficiency and precision are paramount. Therefore, this study constructed a dataset based on images collected by consumer-grade Unmanned Aerial Vehicles (UAVs) and proposed an improved lightweight detection model named CLR-YOLO (Complex-scene Lightweight Rice-detection YOLO) based on the YOLOv11n. The model replaces the original C3k2 module with C3k2-PConv to improve computational efficiency while maintaining feature extraction capability. Additionally, it reconstructs the neck network using the Heterogeneous Selective Feature Pyramid Network (HSFPN) to optimize the handling of features from both large and small targets. Finally, the PConvHead detection head is designed to enhance feature utilization efficiency and reduce both false positives and missed detections in dense rice seedling scenarios. Experimental results demonstrated that CLR-YOLO achieved an average precision (AP@0.5) of 93.9%. While maintaining comparable accuracy, the model reduced parameters to 1.4 M, computational cost to 3.7 GFLOPs, and model size to 2.9 MB—reductions of 46.2%, 41.3%, and 44.2%, respectively, compared to the baseline model. This model provides significant support for rice seedling detection and offers valuable insights to assist agricultural producers in making subsequent decisions. Full article

The high accuracy achieved by deep learning techniques has made them indispensable in computer vision applications. However, their substantial memory demands and high computational complexity limit their deployment in resource-constrained environments. To address this challenge, this study introduces a Feature Enhancement Module (FEM) as part of a unified hybrid compression framework that combines mixed-precision quantization and structured pruning to improve model efficiency. Experimental results on the Tiny ImageNet dataset using ResNet50 and MobileNetV3 architectures demonstrate the strong adaptability and scalability of the proposed approach. Compared with state-of-the-art compression methods, the proposed FEM-based framework achieves up to 6% improvement in Top-1 accuracy, while reducing memory usage by 32.26% and improving inference speed by 66%. Furthermore, the ablation study demonstrates that incorporating the FEM module leads to up to 24% improvement over the baseline model, highlighting its effectiveness. The results further show that FEM effectively preserves inter-channel feature representation stability even under aggressive compression, making it well suited for real-time processing and practical Artificial Intelligence (AI) applications. By maintaining semantic richness while significantly reducing computational cost, the proposed method bridges the gap between high-performance deep models and lightweight, deployable solutions. Overall, the FEM-based hybrid compression framework establishes a scalable and architecture-independent foundation for sustainable deep learning in resource-limited environments. Full article

Girth welds are susceptible to defects under high internal pressure and stress. While phased array ultrasonic testing (PAUT) is widely used for non-destructive evaluation, manual inspection remains inefficient and highly dependent on expertise. Furthermore, existing deep learning models often struggle with low accuracy and high complexity. This paper proposes a PAUT defect classification method based on YOLOv8. First, median filtering is employed for denoising, and the results show that noise is effectively reduced while preserving key features, achieving PSNR values of 35.132, 35.938, and 36.138 for slag inclusion, pores, and lack of fusion (LOF), respectively. Subsequently, the spatial enrichment algorithm (SEA) is applied to enhance image details without amplifying noise, yielding a PSNR of 33.71 and an SSIM of 0.96. Finally, the YOLOv8 model is implemented for defect recognition. Experimental results demonstrate that the proposed approach achieves a superior balance between precision and recall with high reliability. This method offers a robust and efficient solution for automated PAUT evaluation in practical engineering applications. Full article

Addressing the challenges of complex background interference, low lighting conditions, small target recognition, and difficulty in maturity grading in the automated detection of Pleurotus ostreatus, this study proposes a lightweight improved scheme based on color feature enhancement. By collecting 4779 images from five developmental stages in three typical planting environments, including greenhouses and mushroom houses, an HSV hue analysis database was established to determine key hue intervals [4°, 38°] or [110°, 155°] for different environments. Secondly, based on the hue interval distribution of Pleu-rotus ostreatus, YOLOv13 was used as the base model, with the addition of an HSV hue mask as the fourth channel to improve the input layer. The custom ColorWeight module was used to enhance color feature expression; the hypergraph computation module was improved to enhance feature correlation; and the neck network incorporated the StockenAttention module to improve the ability to capture maturity features. The accuracy of the improved model was increased to 89.5% in mAP@0.5 (+3.3%), surpassing the mainstream YOLOv8n-12n series. Efficiency optimization achieved real-time detection at 12.58 FPS on the RTX3090Ti platform. In practical applications, the accuracy of maturity recognition was significantly improved, with a 73.6% decrease in the misclassification rate of maturity and a reduction in missed detections, achieving an F1 score of 0.91. In conclusion, through the deep integration of Hue features and deep learning models, while ensuring lightweight deployment (with only a 10.5% increase in parameter count), the accuracy and practicality of Pleurotus ostreatus detection were significantly improved, providing an effective solution for intelligent mushroom house management. Full article

With the continuous advancement of polarization spectral sensing technology, multi-band polarization image fusion has emerged as a novel approach to image fusion. By integrating spectral and polarization information, this method overcomes the limitations of relying on a single information source and significantly improves overall image quality. To address this, this paper proposes a new polarization spectral fusion algorithm. First, feature matching is employed to achieve pixel-level spatial alignment of multi-band polarization images. Then, a fusion strategy based on multi-scale decomposition and singular value decomposition is adopted to preserve structural information and fine details. Subsequently, fractional-order processing and guided filtering are applied to enhance details and suppress noise. Finally, a progressive reconstruction from low to high scales is performed to ensure hierarchical consistency and information integrity throughout the fusion process. In addition, spectral information is utilized for color restoration, enabling the final image to achieve high spatial resolution while maintaining natural and rich color representation.Experimental results demonstrate that the proposed method effectively integrates features from different spectral bands and polarization information while preserving maximum similarity, leading to significant improvements in both image quality and detail representation. Full article

Images obtained from imaging instruments can endure issues such as high degradation, color distortion, and weak brightness. Effective systems for enhancing these images are critically required. To improve the image quality, herein, we propose two filters based on simple functions, including cosine, sine, hyperbolic secant, and the inverse of hyperbolic cosecant. These filters are used for enhancing the images obtained from a fluorescence imaging system, a near-infrared camera, and low-light condition. The contrast is increased while the image quality is improved. They perform better than a matched filter. Moreover, the combination of our filters with the filter based on the watershed algorithm or the matched filter can be used to extract the marginal features from images generated under water environment. Furthermore, their application in image fusion is explored. Our designed filters may be potentially used for future applications on target identification and tracking. Full article

Cloud detection is essential for optical remote sensing data preprocessing. However, hyperspectral cloud detection datasets remain scarce, suffering from issues such as limited spectral coverage, small annotation scales, and a lack of scene diversity, which hinders the development of hyperspectral cloud detection algorithms. To address this, this paper constructs CloudAHSI—a multi-source hyperspectral cloud detection dataset for global complex scenes—based on the Advanced Hyperspectral Imager (AHSI) aboard the GF-5 01 satellite. The dataset comprises 45 original scenes and enhanced sub-scenes, achieving full-spectrum coverage from 400 to 2500 nm. Through a semi-supervised annotation framework combining “spectral prior-based rough labeling and manual refinement,” the dataset provides pixel-level labels for thick clouds, thin clouds, and non-cloud areas, with scenes further categorized by cloud coverage and primary land cover types. Experiments demonstrate that CloudAHSI effectively supports deep learning models in cloud detection tasks over complex surface backgrounds, particularly showing significant data value in the detection and evaluation of thin clouds, thereby meeting multi-level cloud detection requirements ranging from pixel segmentation to scene understanding. The release of this dataset provides a critical data foundation for overcoming spectral confusion bottlenecks in hyperspectral cloud detection and advancing the utilization of full-spectrum remote sensing information. Full article

The paramount value of image fusion is manifested in effectively enhancing downstream tasks. However, compatibility with subsequent tasks is compromised due to the semantic deficiency of fusion representations generated by current approaches. To mitigate this limitation, a semantic-guided multi-level collaborative fusion network is proposed, termed DSIFuse. By leveraging semantic priors and global context extracted from auxiliary segmentation branches, a multi-level interaction space is constructed to explicitly refine cross-modal features. Specifically, a cross-modal feature correction mechanism is designed to enhance semantic alignment by injecting complementary visible–infrared information at each layer, while a three-level interaction strategy gradually integrates unimodal features and semantic maps to generate semantically enriched representations. To mitigate semantic information loss during image reconstruction, a semantic compensation block is employed, incorporating interactive representations from prior layers and global semantic maps into the multi-scale decoder. Finally, the overall loss integrates semantic supervision, gradient, and intensity loss. Experiments conducted on public datasets indicate that clear fusion images are generated by DSIFuse, with improved structural consistency and reduced artifacts. Under a unified benchmark, the fused representations subsequently yield improved performance in downstream object detection tasks. Full article

Iron-based nanoparticles, particularly iron oxide nanostructures (IONPs), have emerged as versatile and clinically relevant platforms for drug delivery and theranostic applications. Among these, superparamagnetic iron oxide nanoparticles (SPIONs), including magnetite (Fe₃O₄) and maghemite (γ-Fe₂O₃), are the most extensively investigated due to their biocompatibility, magnetic responsiveness, and established safety profiles. Their unique superparamagnetic behavior enables external magnetic-field-guided targeting, magnetic resonance imaging (MRI) contrast enhancement, and magnetically triggered hyperthermia, enabling simultaneous diagnosis and therapy. Surface functionalization with polymers, silica, lipids, peptides, and biomolecules further improves colloidal stability, circulation time, targeting specificity, and controlled drug release. Core–shell architectures and multifunctional hybrid systems have expanded the therapeutic scope of iron nanoparticles, integrating chemotherapy, gene delivery, photothermal therapy, and Fenton reaction–mediated catalytic therapy. Despite promising preclinical outcomes, challenges remain regarding long-term biosafety, oxidative stress induction, biodistribution, large-scale reproducibility, and regulatory translation. This review summarizes the physicochemical properties, synthesis strategies, surface-engineering approaches, drug-loading mechanisms, and biomedical applications of iron-based nanoparticles, highlighting recent advances in multifunctional and peptide-functionalized systems. Critical considerations for clinical translation and future perspectives in precision nanomedicine are also discussed. Full article