Dataset-level color augmentation and multi-scale exploration methods for polyp segmentation DOI

Haipeng Chen,

Honghong Ju, Jun Qin

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125395 - 125395

Published: Sept. 1, 2024

Language: Английский

A survey on deep learning for polyp segmentation: techniques, challenges and future trends DOI Creative Commons

Jiaxin Mei,

Tao Zhou,

Kaiwen Huang

et al.

Visual Intelligence, Journal Year: 2025, Volume and Issue: 3(1)

Published: Jan. 3, 2025

Language: Английский

Citations

7
Semantic-guided complementary fusion network for salient object detection DOI

Kunqian Yang,

Chuan He

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129383 - 129383

Published: Jan. 1, 2025

Language: Английский

Citations

0
ETDformer: an effective transformer block for segmentation of intracranial hemorrhage DOI

Wanyuan Gong,

Yanmin Luo, Fuxing Yang

et al.

Medical & Biological Engineering & Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

Language: Английский

Citations

0
Synergistic Multi-Granularity Rough Attention UNet for Polyp Segmentation DOI Creative Commons
Jing Wang, C. S. Lim

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(4), P. 92 - 92

Published: March 21, 2025

Automatic polyp segmentation in colonoscopic images is crucial for the early detection and treatment of colorectal cancer. However, complex backgrounds, diverse morphologies, ambiguous boundaries make this task difficult. To address these issues, we propose Synergistic Multi-Granularity Rough Attention U-Net (S-MGRAUNet), which integrates three key modules: Hybrid Filtering (MGHF) module extracting multi-scale contextual information, Dynamic Granularity Partition Synergy (DGPS) enhancing polyp-background differentiation through adaptive feature interaction, (MGRA) mechanism further optimizing boundary recognition. Extensive experiments on ColonDB CVC-300 datasets demonstrate that S-MGRAUNet significantly outperforms existing methods while achieving competitive results Kvasir-SEG ClinicDB datasets, validating its accuracy, robustness, generalization capability, all effectively reducing computational complexity. This study highlights value multi-granularity extraction attention mechanisms, providing new insights practical guidance advancing theories medical image segmentation.

Language: Английский

Citations

0
Multi-stage feature fusion network for polyp segmentation DOI
Guangzu Lv, Bin Wang, Cunlu Xu

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113034 - 113034

Published: March 1, 2025

Language: Английский

Citations

0
Selective Guidance Network with edge and texture awareness for polyp segmentation DOI
Qiaohong Chen,

Zhenyang Xu,

Xian Fang

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127772 - 127772

Published: April 1, 2025

Language: Английский

Citations

0
Rethinking Polyp Segmentation from the Perspectives of Matching Views and Seeking Camouflage DOI

Zhengfang Jiang,

Haipeng Chen, Yongping Yang

et al.

Multimedia Systems, Journal Year: 2025, Volume and Issue: 31(3)

Published: May 7, 2025

Language: Английский

Citations

0
A Wavelet-Enhanced Boundary Aware Network with dynamic fusion for polyp segmentation DOI

Y. Y. Wang,

Qi Tian, Jinghui Chu

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 130259 - 130259

Published: May 1, 2025

Language: Английский

Citations

0
Model-agnostic personalized adaptation for segment anything model DOI
Juncheng Wang, Lei Shang, Lu Wang

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 130424 - 130424

Published: May 1, 2025

Language: Английский

Citations

0
FABRF-Net: A frequency-aware boundary and region fusion network for breast ultrasound image segmentation DOI
Yan Liu, Yan Yang, Yongquan Jiang

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103299 - 103299

Published: May 1, 2025

Language: Английский

Citations

0