SCABNet : A Novel Polyp Segmentation Network With Spatial‐Gradient Attention and Channel Prioritization
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| Natura: | Artículo Open Access |
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Wiley
2025
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| _version_ | 1867017204044660736 |
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| author | Khaled ELKarazle Valliappan Raman Caslon Chua Patrick Then |
| author_facet | Khaled ELKarazle Valliappan Raman Caslon Chua Patrick Then Khaled ELKarazle Valliappan Raman Caslon Chua Patrick Then |
| collection | Wiley Open Access |
| contents | SCABNet : A Novel Polyp Segmentation Network With Spatial‐Gradient Attention and Channel Prioritization Khaled ELKarazle Valliappan Raman Caslon Chua Patrick Then International Journal of Imaging Systems and Technology ABSTRACT Current colorectal polyps detection methods often struggle with efficiency and boundary precision, especially when dealing with polyps of complex shapes and sizes. Traditional techniques may fail to precisely define the boundaries of these polyps, leading to suboptimal detection rates. Furthermore, flat and small polyps often blend into the background due to their low contrast against the mucosal wall, making them even more challenging to detect. To address these challenges, we introduce SCABNet, a novel deep learning architecture for the efficient detection of colorectal polyps. SCABNet employs an encoder‐decoder structure with three novel blocks: the Feature Enhancement Block (FEB), the Channel Prioritization Block (CPB), and the Spatial‐Gradient Boundary Attention Block (SGBAB). The FEB applies dilation and spatial attention to high‐level features, enhancing their discriminative power and improving the model's ability to capture complex patterns. The CPB, an efficient alternative to traditional channel attention blocks, assigns prioritization weights to diverse feature channels. The SGBAB replaces conventional boundary attention mechanisms with a more efficient solution that focuses on the spatial attention of the feature map. It employs a Jacobian‐based approach to construct learned convolutions on both vertical and horizontal components of the feature map. This allows the SGBAB to effectively understand the changes in the feature map across different spatial locations, which is crucial for detecting the boundaries of complex‐shaped polyps. These blocks are strategically embedded within the network's skip connections, enhancing the model's boundary detection capabilities without imposing excessive computational demands. They exploit and enhance features at three levels: high, mid, and low, thereby ensuring the detection of a wide range of polyps. SCABNet has been trained on the Kvasir‐SEG and CVC‐ClinicDB datasets and evaluated on multiple datasets, demonstrating superior results. The code is available on: https://github.com/KhaledELKarazle97/SCABNet . 10.1002/ima.70039 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/ima.70039 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_ima_70039 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | SCABNet : A Novel Polyp Segmentation Network With Spatial‐Gradient Attention and Channel Prioritization Khaled ELKarazle Valliappan Raman Caslon Chua Patrick Then International Journal of Imaging Systems and Technology SCABNet : A Novel Polyp Segmentation Network With Spatial‐Gradient Attention and Channel Prioritization Khaled ELKarazle Valliappan Raman Caslon Chua Patrick Then International Journal of Imaging Systems and Technology ABSTRACT Current colorectal polyps detection methods often struggle with efficiency and boundary precision, especially when dealing with polyps of complex shapes and sizes. Traditional techniques may fail to precisely define the boundaries of these polyps, leading to suboptimal detection rates. Furthermore, flat and small polyps often blend into the background due to their low contrast against the mucosal wall, making them even more challenging to detect. To address these challenges, we introduce SCABNet, a novel deep learning architecture for the efficient detection of colorectal polyps. SCABNet employs an encoder‐decoder structure with three novel blocks: the Feature Enhancement Block (FEB), the Channel Prioritization Block (CPB), and the Spatial‐Gradient Boundary Attention Block (SGBAB). The FEB applies dilation and spatial attention to high‐level features, enhancing their discriminative power and improving the model's ability to capture complex patterns. The CPB, an efficient alternative to traditional channel attention blocks, assigns prioritization weights to diverse feature channels. The SGBAB replaces conventional boundary attention mechanisms with a more efficient solution that focuses on the spatial attention of the feature map. It employs a Jacobian‐based approach to construct learned convolutions on both vertical and horizontal components of the feature map. This allows the SGBAB to effectively understand the changes in the feature map across different spatial locations, which is crucial for detecting the boundaries of complex‐shaped polyps. These blocks are strategically embedded within the network's skip connections, enhancing the model's boundary detection capabilities without imposing excessive computational demands. They exploit and enhance features at three levels: high, mid, and low, thereby ensuring the detection of a wide range of polyps. SCABNet has been trained on the Kvasir‐SEG and CVC‐ClinicDB datasets and evaluated on multiple datasets, demonstrating superior results. The code is available on: https://github.com/KhaledELKarazle97/SCABNet . 10.1002/ima.70039 http://creativecommons.org/licenses/by/4.0/ |
| title | SCABNet : A Novel Polyp Segmentation Network With Spatial‐Gradient Attention and Channel Prioritization |
| topic | International Journal of Imaging Systems and Technology |
| url | https://onlinelibrary.wiley.com/doi/10.1002/ima.70039 |