Efficient and effective perception and aggregation mechanisms are crucial in medical image segmentation, especially in scenarios with limited computing resources. However, existing methods are usually accompanied by high computational costs. At the same time, the ubiquitous co-occurrence phenomenon makes it difficult for the model to effectively distinguish target features from interfering background information. To address the above problems, this paper proposes an LSDF-UNet segmentation model. First, a “looking at the big-focusing on the small” dynamic fusion mechanism is adopted to achieve lightweight and efficient feature enhancement under linear complexity. Secondly, a dual-size patch frequency aware module (DPFA) is designed, combined with a frequency-aware block (FAB) and a dual-scale patch partitioning strategy to separate high-frequency details and low-frequency contours in the frequency domain, suppress cooccurrence noise, and significantly improve boundary discrimination. Through extensive experiments on three benchmark medical image datasets, it is demonstrated that our method achieves state-of-the-art performance and effectiveness. Our released code is available at https://github.com/Submission2025/LSDF-UNet.

Current RGB-D salient object detection (SOD) methods predominantly rely on simplistic cross-modal fusion strategies that inadequately model intrinsic inter-modal correlations and underutilize hierarchical representations in U-shaped architecture. This often leads to incomplete predictions and boundary ambiguities due to insufficient exploitation of low-level spatial details. To address these limitations, we propose the Cross-Modal Consistency and Aggregative Decoding in enhanced UNet (CMAD-UNet), a novel architecture designed to advance saliency detection through synergistic multi-modal correlation fusion and adaptive feature aggregation. Specifically, this network integrates three core modules: Enhanced Atrous Spatial Pyramid Pooling Module (EnASP) embedded in a dual-branch hybrid Convolutional Neural Networks (CNNs) and Transformer encoder expands the receptive field to capture global contextual information; Correlation-Perceptive Fusion Module (CPFM) constructs learnable affinity matrices through contrastive consistency learning, effectively suppressing cross-modal noise while aligning feature distributions; Selective Aggregation Decoding Module (SADM) dynamically weights multi-level features via channel-spatial attention mechanisms during upsampling, suppressing noise propagation and generating saliency maps with well-defined boundaries. Through extensive experiments on 8 benchmark datasets evaluated with 5 metrics, CMAD-UNet demonstrates superior performance compared to 14 state-of-the-art approaches.

Medical image segmentation is crucial for clinical decision-making, treatment planning, and disease tracking. Nonetheless, it confronts two significant challenges: the presence of ''soft boundaries'' between the foreground and background exacerbated by poor illumination and low contrast, and the misleading co-occurrence of salient and non-salient objects during the training phase, which complicates the model's accuracy in distinguishing relevant features. To overcome these challenges, we introduce RoDeCon-Net, a novel framework engineered to enhance medical image segmentation. RoDeCon-Net incorporates a Feature Decoupling Unit (FDU) that dynamically separates encoded features into foreground, background, and uncertain regions, using advanced attention mechanisms to refine feature distinction and reduce uncertainty. Additionally, our Contrast-driven Feature Alignment Unit (CFAU) and Cross-layer Feature Cascade Unit (CFCU) synergize to reinforce feature contrasts and promote effective multi-level feature fusion, thus improving the detection of salient objects amidst complex backgrounds and handling various object scales within images. Comprehensive evaluations of RoDeCon-Net on five diverse medical image datasets validate its superior performance and versatility, showcasing its potential to set new benchmarks in medical image segmentation. Our code is available on https://github.com/ILoveACM-MM/RoDeCon-Net.

Research Highlights • This study proposes a novel approach for enhancing cybersecurity through the integration of GA-AFCM. This method significantly improves the accuracy, efficiency, and adaptability of IDS. • The GA-AFCM technique demonstrates superior performance compared to conventional methods such as K-Means, MKKM-IC, Density Peaks, and GMM. • The proposed method effectively addresses the challenges of security of information in the period of Big Data, achieving the highest detection rate while significantly reducing false positives, thereby enhancing overall system reliability and efficiency. Cybersecurity includes protecting computer networks and systems from unauthorized access, harm, and fraud, employing various techniques and technologies such as barriers, antivirus software, and cryptography. Regular system updates, employee training, and adherence to best practices are crucial for maintaining confidentiality and ensuring reliable IT services in both corporate and public sectors. This paper introduces a GA-AFCM technique, which enhances intrusion detection and cybersecurity tasks by combining the strengths of Genetic Algorithms and Adaptive Fuzzy C-Means Clustering. The study began with data collection and preprocessing using Z-score normalization, followed by feature extraction through Linear Discrimination Analysis (LDA). The GA-AFCM approach was compared with traditional methods such as K-Means, Density Peaks, GMM, and MKKM-IC. The results demonstrate the TPR (91%), FPR (4%) precision (83.56%), accuracy (95.6%), and F1-score (87%) are used to examining and interpreting quickly and dynamically generated data streams efficiently solved by the proposed approach. The GA-AFCM method significantly enhances detection rates to over 95% while substantially reducing false positives, establishing it as a robust solution for cybersecurity in the big data era.