Weiqing Liu^1,#, Bin Li^1,#,*, Lianfang Tian¹, Qianhui Qiu²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.087647 - 28 August 2026

Abstract Self-supervised representation learning can reduce the dependence of three-dimensional (3D) medical image segmentation on dense voxel annotations. In multimodal 3D medical imaging, intensity-reconstruction pre-training provides dense appearance supervision but does not explicitly distinguish the structural regions that determine segmentation boundaries and small targets. A second mismatch arises in scale learning: encoder-decoder networks provide multi-scale feature maps, but they do not explicitly supervise how fine anatomical structures weaken or persist across neighboring scales. To address these mismatches, this study proposes Scale Ladder Consistency (SLC), a structure-aware self-supervised representation learning framework for multimodal 3D medical image segmentation.… More >

Kai Luo^1,2,*, Yuzhi Chen³

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.079382 - 24 August 2026

Abstract Material property variability in carbon fiber-reinforced polymer composites is a major source of uncertainty in quantitative Lamb wave-based delamination imaging. Even minor deviations in elastic properties can alter dispersion characteristics and wave propagation behavior, thereby reducing the reliability of imaging-based assessments. To systematically investigate this effect, the present study examines the influence of subtle material variations on Lamb wave responses through combined numerical modeling and finite element simulations. Time-of-flight features at the excitation frequency are extracted using a continuous wavelet transform with Morlet wavelets, enabling robust identification of mode-dependent arrival information. Within a finite element… More >

Usman Khalid¹, Nikhil Shah¹, Rajesh Kavia², Deepak Batura^2,*

Canadian Journal of Urology, Vol.33, No.4, pp. 735-752, 2026, DOI:10.32604/cju.2026.074820 - 21 August 2026

Abstract Bladder cancer (BCa) diagnosis relies heavily on cystoscopy and imaging. Both have limited sensitivity and accuracy, particularly for muscle-invasive disease. Artificial intelligence (AI) has emerged as a promising tool for improving detection, grading, and staging by extracting imaging features that exceed human perception. We conducted a narrative review of peer-reviewed, English-language studies published between 2015 and 2025. We identified 75 articles and synthesized data from 35 key studies retrieved via PubMed, Google Scholar, Scopus, and Embase. Data were synthesized narratively, emphasizing diagnostic performance, clinical relevance, and study limitations. In cystoscopy, AI models achieved high accuracy… More >

Shilpa Aryal^1,*, Marhisham Che Mood², Sivakumar Sivalingam², Boekhren Karyostyko², Yusoff Ramdzan², Haifa Abdul Latiff², Ming Chern Leong²

Structural and Congenital Heart Disease, Vol.21, No.3, 2026, DOI:10.32604/schd.2026.083109 - 31 July 2026

Abstract Background: Double-outlet right ventricle (DORV) encompasses a spectrum of disorders characterized by both great vessels arising from the right ventricle. This study compared the diagnostic accuracy of multislice computed tomography (MSCT) and transthoracic echocardiography (TTE) in predicting the optimal surgical strategy by evaluating the concordance between preoperative imaging-based decisions and intraoperative findings. Methods: This single-center, retrospective study included 112 patients with DORV (56 MSCT + TTE and 56 TTE only) who underwent surgical correction between January 2010 and December 2024. Agreement between preoperative and intraoperative surgical decisions was assessed by weighted kappa (κ). Diagnostic accuracy was… More >

Karim Gasmi^1,*, Afrah Alanazi², Inam Alanazi², Sahar Almenwer¹, Norah Alanazi¹, Sarah Almaghrabi³, Samia Yahyaoui⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081705 - 30 June 2026

Abstract Cardiac magnetic resonance imaging (MRI) segmentation is an essential aspect of quantitative cardiovascular analysis, facilitating accurate evaluation of ventricular volumes, myocardial mass, and functional parameters. Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC, but they remain challenging to deploy in real-world multi-centre settings. Data privacy laws make it hard to share data across institutions, and differences in imaging protocols and patient populations mean that data is not always distributed in the same way (non-IID). This can have a big impact on how well models work together and how well… More >

Mahmood Ashraf^1,2, Nuha Zamzami³, Shtwai Alsubai⁴, Raed Alharthi⁵, Muhammad Umer^6,*, Yunyoung Nam⁷, Yongwon Cho^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.078738 - 30 June 2026

Abstract Hyperspectral image (HSI) denoising is a crucial preprocessing step that significantly enhances the performance of downstream applications, such as object detection and classification. Whereas deep neural networks have achieved remarkable performance in HSI denoising, many existing models rely mostly on vanilla convolutions, which often fail to capture fine-grained noise patterns and structural details in real-time HSIs. To address these limitations, we propose a novel Center-Difference Convolutional Network (CDCN) designed to effectively suppress various noise types while preserving the inherent structure of HSIs. By leveraging center-difference convolution (CDC), our model captures both gradient and intensity information… More >

Guopeng Fan^1,*, Xuefeng Chen¹, Hao Liu¹, Jiaqing Zheng²

Structural Durability & Health Monitoring, Vol.20, No.4, 2026, DOI:10.32604/sdhm.2026.079234 - 30 June 2026

Abstract Low-frequency ultrasonic array is commonly used to detect interlayer voids located in high-speed railway ballastless track, which is a typical multilayer concrete bonded structure. The difficulty of detection lies in the fact that the total focusing method (TFM) based on a single fixed sound velocity model cannot adapt to the acoustic propagation characteristics of multilayer structures, which is prone to generating artifacts. In addition, the long duration of low-frequency ultrasonic pulses is prone to causing significant deviations in defect localization. To address these issues, a theoretical model of the layered bonded structure is proposed. The… More >

Xinjie Yao¹, Junjie Zhu², Tao Hong^3,4, Dengyu Zhao⁵, Weikai Liu⁶, Guangsheng Xie^7,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.075316 - 15 June 2026

Abstract The attention mechanism, as a key technology for enhancing the performance of deep learning, is gaining increasingly widespread attention in medical image analysis due to its ability to focus on critical features and suppress redundant information. In recent years, the continuous evolution of attention methods has significantly improved their accuracy and robustness in key medical tasks such as lesion detection, tissue segmentation, and multimodal fusion, providing crucial support for building reliable clinical decision support systems. This paper systematically reviews the advances in attention-based methods for medical image analysis, comparing their performance with mainstream models like… More >

Chan-Min Hsu¹, Shang-Ru Yang¹, Yi-Ju Lee¹, An-Chi Wei^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076098 - 08 May 2026

Abstract U-Net, a fully convolutional neural network (FCNN) with U-shaped features, has demonstrated significant success in biomedical image segmentation. However, the locality of convolution operations in the U-Net limits its ability to learn long-range dependencies. Transformers, originally developed for natural language processing, have recently been adapted for image segmentation because of their global self-attention mechanisms. Inspired by the long-range feature learning capability of transformers, we propose Dense-Transformer (DenT), an architecture designed for volumetric microscopy image segmentation. DenT incorporates transformers as encoders within each convolutional layer to capture global contextual information. Additionally, dense skip connections at multiple More >

Ghada Atteia¹, Abdulaziz Altamimi², Nihal Abuzinadah³, Khaled Alnowaiser⁴, Muhammad Umer^5,*, Yunyoung Nam⁶, Yongwon Cho^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078510 - 27 April 2026

Abstract Cardiovascular diseases (CVD) remain the leading cause of global mortality, making early and accurate diagnosis essential for improving patient outcomes. However, most existing deep learning approaches address cardiac image segmentation or disease classification independently, limiting their effectiveness in complex clinical decision-making scenarios. In this study, we propose an explainable spatio-temporal deep learning framework that integrates segmentation-guided representation learning with efficient temporal modeling for automated CVD detection. The proposed architecture incorporates the Segment Anything Model for Medical Imaging in 2D (SAM-Med2D) to achieve accurate cardiac structure segmentation, followed by Mamba-based temporal feature extraction and Transformer-driven spatial… More >