MFAR-Net: Multi-level feature interaction and Dual-Dimension adaptive reinforcement network for breast lesion segmentation in ultrasound images

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126727 - 126727

Опубликована: Фев. 1, 2025

Язык: Английский

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A frequency attention-embedded network for polyp segmentation

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 10, 2025

Gastrointestinal polyps are observed and treated under endoscopy, so there presents significant challenges to advance endoscopy imaging segmentation of polyps. Current methodologies often falter in distinguishing complex polyp structures within diverse (mucosal) tissue environments. In this paper, we propose the Frequency Attention-Embedded Network (FAENet), a novel approach leveraging frequency-based attention mechanisms enhance accuracy significantly. FAENet ingeniously segregates processes image data into high low-frequency components, enabling precise delineation boundaries internal by integrating intra-component cross-component mechanisms. This method not only preserves essential edge details but also refines learned representation attentively, ensuring robust across varied conditions. Comprehensive evaluations on two public datasets, Kvasir-SEG CVC-ClinicDB, demonstrate FAENet's superiority over several state-of-the-art models terms Dice coefficient, Intersection Union (IoU), sensitivity, specificity. The results affirm that advanced significantly improve quality, outperforming traditional contemporary techniques. success indicates its potential revolutionize clinical practices, fostering diagnosis efficient treatment gastrointestinal

Язык: Английский

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Label-Decoupled Medical Image Segmentation With Spatial-Channel Graph Convolution and Dual Attention Enhancement

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2024, Номер 28(5), С. 2830 - 2841

Опубликована: Фев. 20, 2024

Deep learning-based methods have been widely used in medical image segmentation recently. However, existing works are usually difficult to simultaneously capture global long-range information from images and topological correlations among feature maps. Further, often suffer blurred target edges. Accordingly, this paper proposes a novel framework named label-decoupled network with spatial-channel graph convolution dual attention enhancement mechanism (LADENet for short). It constructs learnable adjacency matrices utilizes convolutions effectively on spatial locations dependencies between different channels an image. Then strategy based distance transformation is introduced decouple original label into body edge supervising the branch branch. Again, mechanism, designing block branch, built promote learning ability of region boundary features. Besides, interactor devised fully consider interaction branches improve performance. Experiments benchmark datasets reveal superiority LADENet compared state-of-the-art approaches.

Язык: Английский

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Multi-view orientational attention network combining point-based affinity for polyp segmentation

Expert Systems with Applications, Год журнала: 2024, Номер 249, С. 123663 - 123663

Опубликована: Март 19, 2024

Язык: Английский

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FMCA-Net: A feature secondary multiplexing and dilated convolutional attention polyp segmentation network based on pyramid vision transformer

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 125419 - 125419

Опубликована: Сен. 1, 2024

Язык: Английский

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An Edge-Enhanced Network for Polyp Segmentation

Bioengineering, Год журнала: 2024, Номер 11(10), С. 959 - 959

Опубликована: Сен. 25, 2024

Colorectal cancer remains a leading cause of cancer-related deaths worldwide, with early detection and removal polyps being critical in preventing disease progression. Automated polyp segmentation, particularly colonoscopy images, is challenging task due to the variability appearance low contrast between surrounding tissues. In this work, we propose an edge-enhanced network (EENet) designed address these challenges by integrating two novel modules: covariance attention (CEEA) cross-scale edge enhancement (CSEE) modules. The CEEA module leverages covariance-based enhance boundary detection, while CSEE bridges multi-scale features preserve fine-grained details. To further improve accuracy introduce hybrid loss function that combines cross-entropy edge-aware loss. Extensive experiments show EENet achieves Dice score 0.9208 IoU 0.8664 on Kvasir-SEG dataset, surpassing state-of-the-art models such as Polyp-PVT PraNet. Furthermore, it records 0.9316 0.8817 CVC-ClinicDB demonstrating its strong potential for clinical application segmentation. Ablation studies validate contribution

Язык: Английский

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InCoLoTransNet: An Involution-Convolution and Locality Attention-Aware Transformer for Precise Colorectal Polyp Segmentation in GI Images

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

Gastrointestinal (GI) disease examination presents significant challenges to doctors due the intricate structure of human digestive system. Colonoscopy and wireless capsule endoscopy are most commonly used tools for GI examination. However, large amount data generated by these technologies requires expertise intervention identification, making manual analysis a very time-consuming task. Thus, development computer-assisted system is highly desirable assist clinical professionals in decisions low-cost effective way. In this paper, we introduce novel framework called InCoLoTransNet, designed polyp segmentation. The study based on transformer convolution-involution neural network, following encoder-decoder architecture. We employed vision encoder section focus global context, while decoder involves collaboration resampling features. Involution enhances model's ability adaptively capture spatial contextual information, convolution focuses local leading more accurate feature extraction. essential features captured passed through two skip connection pathways. CBAM module refines passes them block, leveraging attention mechanisms emphasize relevant information. Meanwhile, locality self-attention pass involution reinforcing regions. Experiments were conducted five public datasets: CVC-ClinicDB, CVC-ColonDB, Kvasir-SEG, Etis-LaribPolypDB, CVC-300. results obtained InCoLoTransNet optimal when compared with 15 state-of-the-art methods segmentation, achieving highest mean dice score 93% CVC-ColonDB 90% intersection over union, outperforming methods. Additionally, distinguishes itself terms segmentation generalization performance. It achieved high scores coefficient union unseen datasets as follows: 85% 79% 91% 87% CVC-300, 70% respectively.

Язык: Английский

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RootEx: An automated method for barley root system extraction and evaluation

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 230, С. 110030 - 110030

Опубликована: Фев. 6, 2025

Язык: Английский

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SCABNet: A Novel Polyp Segmentation Network With Spatial‐Gradient Attention and Channel Prioritization

International Journal of Imaging Systems and Technology, Год журнала: 2025, Номер 35(2)

Опубликована: Фев. 6, 2025

ABSTRACT Current colorectal polyps detection methods often struggle with efficiency and boundary precision, especially when dealing of complex shapes sizes. Traditional techniques may fail to precisely define the boundaries these polyps, leading suboptimal rates. Furthermore, flat small blend into background due their low contrast against mucosal wall, making them even more challenging detect. To address challenges, we introduce SCABNet, a novel deep learning architecture for efficient polyps. SCABNet employs an encoder‐decoder structure three blocks: Feature Enhancement Block (FEB), Channel Prioritization (CPB), Spatial‐Gradient Boundary Attention (SGBAB). The FEB applies dilation spatial attention high‐level features, enhancing discriminative power improving model's ability capture patterns. CPB, alternative traditional channel blocks, assigns prioritization weights diverse feature channels. SGBAB replaces conventional mechanisms solution that focuses on map. It Jacobian‐based approach construct learned convolutions both vertical horizontal components This allows effectively understand changes in map across different locations, which is crucial detecting complex‐shaped These blocks are strategically embedded within network's skip connections, capabilities without imposing excessive computational demands. They exploit enhance features at levels: high, mid, low, thereby ensuring wide range has been trained Kvasir‐SEG CVC‐ClinicDB datasets evaluated multiple datasets, demonstrating superior results. code available on: https://github.com/KhaledELKarazle97/SCABNet .

Язык: Английский

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Dynamic Frequency-Decoupled Refinement Network for Polyp Segmentation

Bioengineering, Год журнала: 2025, Номер 12(3), С. 277 - 277

Опубликована: Март 11, 2025

Polyp segmentation is crucial for early colorectal cancer detection, but accurately delineating polyps challenging due to their variations in size, shape, and texture low contrast with surrounding tissues. Existing methods often rely solely on spatial-domain processing, which struggles separate high-frequency features (edges, textures) from low-frequency ones (global structures), leading suboptimal performance. We propose the Dynamic Frequency-Decoupled Refinement Network (DFDRNet), a novel framework that integrates frequency-domain processing. DFDRNet introduces Frequency Adaptive Decoupling (FAD) module, dynamically separates high- components, (FAR) refines these components before fusing them spatial enhance accuracy. Embedded within U-shaped encoder–decoder framework, achieves state-of-the-art performance across three benchmark datasets, demonstrating superior robustness efficiency. Our extensive evaluations ablation studies confirm effectiveness of balancing accuracy computational

Язык: Английский

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