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  • Open Access

    ARTICLE

    Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

    Hengyang Liu1, Yang Yuan1,*, Pengcheng Ren1, Chengyun Song1, Fen Luo2

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 543-560, 2025, DOI:10.32604/cmc.2024.056478 - 03 January 2025

    Abstract Existing semi-supervised medical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch. However, current copy-paste methods have three limitations: (1) training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information; (2) low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data; (3) the segmentation performance in low-contrast and local regions is less than optimal. We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy (SADT), which enhances feature diversity and learns high-quality features to overcome these problems. To be more… More >

  • Open Access

    ARTICLE

    Stability of a Viscous Liquid Film in a Rotating Cylindrical Cavity under Angular Vibrations

    Victor Kozlov1,*, Alsu Zimasova1, Nikolai Kozlov2

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.12, pp. 2693-2707, 2024, DOI:10.32604/fdmp.2024.052398 - 23 December 2024

    Abstract The behavior of a viscous liquid film on the wall of a rapidly rotating cylinder subjected to angular vibrations is experimentally studied. The cavity is filled with an immiscible low-viscosity liquid of lower density. In the absence of vibrations, the high viscosity liquid covers the inner surface of the cylinder with a relatively thin axisymmetric film; the low-viscosity liquid is located in the cavity interior. It is found that with an increase in the amplitude of rotational vibrations, the axisymmetric interphase boundary loses stability. An azimuthally periodic 2D “frozen wave” appears on the film surface… More >

  • Open Access

    ARTICLE

    Position-Aware and Subgraph Enhanced Dynamic Graph Contrastive Learning on Discrete-Time Dynamic Graph

    Jian Feng*, Tian Liu, Cailing Du

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2895-2909, 2024, DOI:10.32604/cmc.2024.056434 - 18 November 2024

    Abstract Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph representation learning to eliminate the dependence of labels. However, existing studies neglect positional information when learning discrete snapshots, resulting in insufficient network topology learning. At the same time, due to the lack of appropriate data augmentation methods, it is difficult to capture the evolving patterns of the network effectively. To address the above problems, a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic graphs. Firstly, the global snapshot is built based on the historical snapshots… More >

  • Open Access

    ARTICLE

    Robust and Discriminative Feature Learning via Mutual Information Maximization for Object Detection in Aerial Images

    Xu Sun, Yinhui Yu*, Qing Cheng

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4149-4171, 2024, DOI:10.32604/cmc.2024.052725 - 12 September 2024

    Abstract Object detection in unmanned aerial vehicle (UAV) aerial images has become increasingly important in military and civil applications. General object detection models are not robust enough against interclass similarity and intraclass variability of small objects, and UAV-specific nuisances such as uncontrolled weather conditions. Unlike previous approaches focusing on high-level semantic information, we report the importance of underlying features to improve detection accuracy and robustness from the information-theoretic perspective. Specifically, we propose a robust and discriminative feature learning approach through mutual information maximization (RD-MIM), which can be integrated into numerous object detection methods for aerial images.… More >

  • Open Access

    ARTICLE

    Research on Improved MobileViT Image Tamper Localization Model

    Jingtao Sun1,2, Fengling Zhang1,2,*, Huanqi Liu1,2, Wenyan Hou1,2

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3173-3192, 2024, DOI:10.32604/cmc.2024.051705 - 15 August 2024

    Abstract As image manipulation technology advances rapidly, the malicious use of image tampering has alarmingly escalated, posing a significant threat to social stability. In the realm of image tampering localization, accurately localizing limited samples, multiple types, and various sizes of regions remains a multitude of challenges. These issues impede the model’s universality and generalization capability and detrimentally affect its performance. To tackle these issues, we propose FL-MobileViT-an improved MobileViT model devised for image tampering localization. Our proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain, and captures richer traces of tampering… More >

  • Open Access

    ARTICLE

    Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

    Bing Li1,2,*, Liangyu Wang1, Xia Liu1,2, Hongbin Fan1, Bo Wang3, Shoudi Tong1

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1543-1561, 2024, DOI:10.32604/cmc.2024.052009 - 18 July 2024

    Abstract Nuclear magnetic resonance imaging of breasts often presents complex backgrounds. Breast tumors exhibit varying sizes, uneven intensity, and indistinct boundaries. These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation. Thus, we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms. Initially, the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs. Subsequently, we devise a fusion network incorporating multi-scale features and boundary attention mechanisms for breast tumor segmentation. We incorporate multi-scale parallel dilated convolution modules into… More >

  • Open Access

    ARTICLE

    Efficient Clustering Network Based on Matrix Factorization

    Jieren Cheng1,3, Jimei Li1,3,*, Faqiang Zeng1,3, Zhicong Tao1,3, Yue Yang2,3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 281-298, 2024, DOI:10.32604/cmc.2024.051816 - 18 July 2024

    Abstract Contrastive learning is a significant research direction in the field of deep learning. However, existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods. To address these challenges, we propose the Efficient Clustering Network based on Matrix Factorization (ECN-MF). Specifically, we design a batched low-rank Singular Value Decomposition (SVD) algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data. Additionally, we design a Mutual Information-Enhanced More >

  • Open Access

    ARTICLE

    A New Framework for Software Vulnerability Detection Based on an Advanced Computing

    Bui Van Cong1, Cho Do Xuan2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3699-3723, 2024, DOI:10.32604/cmc.2024.050019 - 20 June 2024

    Abstract The detection of software vulnerabilities written in C and C++ languages takes a lot of attention and interest today. This paper proposes a new framework called DrCSE to improve software vulnerability detection. It uses an intelligent computation technique based on the combination of two methods: Rebalancing data and representation learning to analyze and evaluate the code property graph (CPG) of the source code for detecting abnormal behavior of software vulnerabilities. To do that, DrCSE performs a combination of 3 main processing techniques: (i) building the source code feature profiles, (ii) rebalancing data, and (iii) contrastive… More >

  • Open Access

    ARTICLE

    Pairwise Reversible Data Hiding for Medical Images with Contrast Enhancement

    Isaac Asare Boateng1,2,*, Lord Amoah2, Isogun Toluwalase Adewale3

    Journal of Information Hiding and Privacy Protection, Vol.6, pp. 1-19, 2024, DOI:10.32604/jihpp.2024.051354 - 24 June 2024

    Abstract Contrast enhancement in medical images has been vital since the prevalence of image representations in healthcare. In this research, the PRDHMCE (pairwise reversible data hiding for medical images with contrast enhancement) algorithm is proposed as an automatic contrast enhancement (CE) method for medical images based on region of interest (ROI) and non-region of interest (NROI). The PRDHMCE algorithm strategically enhances the ROI after segmentation using histogram stretching and data embedding. An initial histogram evaluation compares histogram bins with their neighbours to select the bin with the maximum pixel count. The selected bin is set as More >

  • Open Access

    ARTICLE

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

    Samar M. Alqhtani1, Toufique A. Soomro2,*, Faisal Bin Ubaid3, Ahmed Ali4, Muhammad Irfan5, Abdullah A. Asiri6

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475 - 20 May 2024

    Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More > Graphic Abstract

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

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