Medical image segmentation faces two challenges: First, the contour structures of salient objects and background details vary significantly in medical images of different modalities. Second, the salient and non-salient objects often exhibit misleading co-occurrence phenomena. To overcome these challenges, we propose ASC-Seg. It adopts the Adaptive Structure Encoder (ASE) to capture the global structure of salient objects via parallel Mamba, and uses deformable learning to adaptively locate contour details in complex backgrounds in medical images of different modalities. Plus, our Cross-scale Noise Suppression Decoder (CNSD) uses noise suppression strategy and efficient attention mechanism to suppress non-salient regions and highlight critical salient regions, thereby distinguishing salient and nonsalient objects. Extensive experiments of ASC-Seg on 5 medical image datasets verify its superior performance. Our code is available on https://github.com/SubmissionPaper2025/ASC-Seg

Salient object detection in optical remote sensing images (ORSI-SOD) aims to automatically identify the most visually prominent objects or regions in remote sensing images. However, due to the diverse orientations and varying scales of salient objects, as well as cluttered backgrounds, it remains a challenging task. To tackle these issues, we propose a direction-aware attention and semantic guidance network (DASGNet), a novel framework designed to enhance sensitivity to both orientation and multi-scale information while improving the depiction of boundaries in complex scenes. DASGNet integrates two key modules: a multi-scale direction-aware attention module (MDAM) and a semantic-guided edge reconstruction module (SERM). MDAM combines the attention mechanism with orientation information, effectively suppressing redundant information while capturing multi-scale orientation features. SERM employs 3D convolution to construct a stereoscopic receptive field, facilitating the integration of high-level semantic information across scales to guide the reconstruction of low-level texture information and thereby achieving precise edge delineation, particularly in complex scenes. Extensive experiments on three benchmark datasets demonstrate that DASGNet outperforms 14 state-of-the-art methods, achieving significant improvements in both accuracy and precision.

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.