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RCNU-Net: Reparameterized convolutional network with convolutional block attention module for improved polyp image segmentation
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DOI：10.1016/j.bspc.2024.106138.png)
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摘要
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In the context of medical image segmentation, the segmentation of polyps holds significant importance for early cancer screening and preoperative planning. Polyp images encapsulate substantial semantic information, including the main body of the colon, internal wall folds, and the polyp itself. These components exhibit numerous similar features, while the characteristics of polyps vary significantly in different locations. This paper proposes an improved U -Net neural network model designed to address the issue of unbalanced segmentation accuracy and generalization capability in existing models. The incorporation of a reparameterization module as the backbone network integrates multiscale features while adopting a training -prediction model separation pattern ensures the accuracy of the model. To avoid compromising global spatial information and simultaneously enhance the global perceptual capability, we employ a convolutional block attention module to compensate for the feature loss generated by skip connections. Additionally, we devise a loss computation method specific to this model, named CDLoss, to achieve more effective gradient optimization and enhance the model's ability to segment polyp boundaries. Our model undergoes comprehensive validation on the Kavirs-SEG and CVC-ClinicDB datasets. The segmentation Intersection over Union (IoU) and Dice values reach 96.56% and 98.16%, respectively. The generalization capability achieves 98.57% and 97.33% in CVC-ClinicDB, surpassing other current state-of-the-art polyp image segmentation models.
Keyword:
U-Net
Reparameterization
RSCNet
CBAM
CDLoss
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
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