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

International Journal of Imaging Systems and Technology, Journal Year: 2025, Volume and Issue: 35(2)

Published: Feb. 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 .

Language: Английский

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Structure-preserving dental plaque segmentation via dynamically complementary information interaction

Multimedia Systems, Journal Year: 2025, Volume and Issue: 31(2)

Published: March 14, 2025

Language: Английский

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Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: March 27, 2025

Introduction Accurate segmentation of lesion tissues in medical microscopic hyperspectral pathological images is crucial for enhancing early tumor diagnosis and improving patient prognosis. However, the complex structure indistinct boundaries present significant challenges achieving precise segmentation. Methods To address these challenges, we propose a novel method named BE-Net. It employs multi-scale strategy edge operators to capture fine details, while incorporating information entropy construct attention mechanisms that further strengthen representation relevant features. Specifically, first Laplacian Gaussian operator convolution boundary feature extraction block, which encodes gradient through improved detection emphasizes channel weights based on weighting. We designed grouped module optimize fusion process between encoder decoder, with goal details emphasizing representations. Finally, spatial block guide model most important locations regions. Result evaluate BE-Net image datasets gastric intraepithelial neoplasia mucosal intestinal metaplasia. Experimental results demonstrate outperforms other state-of-the-art methods terms accuracy preservation. Discussion This advance has implications field MHSIs Our code freely available at https://github.com/sharycao/BE-NET .

Language: Английский

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Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review

The Ewha Medical Journal, Journal Year: 2024, Volume and Issue: 47(4)

Published: Sept. 12, 2024

Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology. This review explores the integration of AI into oncology, highlighting both challenges and opportunities. can improve precision, efficiency, outcomes therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive therapy, enabling predictive analytics. Through analysis large datasets to identify optimal parameters, automate complex tasks, reduce planning time, accuracy. In AI-driven techniques enhance tumor detection segmentation processing data from CT, MRI, PET scans enable precise delineation. beneficial because it allows real-time adjustments plans based on changes in patient anatomy size, thereby improving accuracy effectiveness. Predictive analytics using historical predict potential complications, guiding clinical decision-making more personalized strategies. Challenges adoption oncology include ensuring quality quantity, achieving interoperability standardization, addressing regulatory ethical considerations, overcoming resistance implementation. Collaboration among researchers, clinicians, scientists, industry stakeholders crucial these obstacles. By challenges, drive advancements care operational efficiencies. presents an overview current state insights future directions for research practice.

Language: Английский

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Boundary Aware Microscopic Hyperspectral Pathology Image Segmentation Network Guided by Information Entropy Weight

Published: Jan. 1, 2024

Language: Английский

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