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| Cascading Feature Fusion Polyp Segmentation Network Based on Efficient Additive Attention |
| Li Meng, Zhang Sunjie |
| School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China |
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Abstract To address the problems of insufficient information interaction between local and global and weak correlation of features between neighboring layers at different depths in most polyp segmentation methods, this paper proposed a new network model (PVT-SMCD) based on the Pyramid Vision Transformer and self-attention mechanism cascaded decoder. Firstly, PVTv2 was used as the backbone network to extract image features, and the key information was obtained through the efficient additive attention module to capture the long-distance dependency relationships. Secondly, a multiple kernel convolution enhance blocks were introduced to locate high-level semantic features of polyps, and the obtained featureswere inputted to the cascade decoder to achieve the information interaction between the local and global layers. And lastly, a feature fusion module was used to gradually fuse the features between the two neighboring layers from top to bottom to reduce the information gap between the fused high-dimensional features and the low-dimensional features. The model in this paperwas compared with other eight medical image segmentation networks on five polyp segmentation datasets, mDice on Kvasir and CVC-ClinicDB datasets were 92.3% and 94.5%, mIoU were 87.1% and 89.9%, and the MAE were 0.021 and 0.006, respectively; on CVC-300, mDice and mIoU were 90% and 83.3%, respectively, with an MAE of 0.007; on CVC-ColonDB mDice were81.5%, mIoU was 73.5%, and MAE was 0.028; And on the ETIS dataset, mDice was 78.9%, mIoU was 71.3%, and MAE was0.019. Experimental results indicated that PVT-SMCD achieved superior performance over state-of-the-art methods across most of the evaluation metrics, demonstrating enhanced learning ability and generalization capacity, leading to more precise polyp segmentation outcomes.
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Received: 14 October 2024
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Corresponding Authors:
*E-mail:zhang_sunjie@126.com
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