SFNet: Saliency fast Fourier convolutional Network for medical image segmentation
Article No.: 60, Pages 1 - 7
Abstract
Due to the limitation of local characteristics of convolution operations, the encoder of U-Net cannot effectively capture global context information; furthermore, the skip connections of U-Net fail to capture salient features in image segmentation tasks. Therefore, we put forward a Saliency fast Fourier convolutional Network (SFNet) for medical image segmentation. To begin with, we propose a SCAU attention module, which can highlight both spatial and channel attention for capturing not only global but local information, paying more attention to the whole target regions of an image, and creating strong information association among samples to extract the characteristics of the overall dataset. Subsequently, instead of employing the convolution to set the encoder and decoder, we introduce a Fourier convolution module, FFconv, which owns the non-local receptive field to fulfil cross-scale fusion in the convolution unit properly. Experimental results show that, on BUSI and Kvasir-SEG datasets, the mIOU and F1-score of our network reach 75.08% and 84.75%, respectively; our network greatly promote medical image segmentation performance.
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Index Terms
- SFNet: Saliency fast Fourier convolutional Network for medical image segmentation
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Published In
December 2023
745 pages
ISBN:9798400702051
DOI:10.1145/3595916
Copyright © 2023 ACM.
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Published: 01 January 2024
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- Refereed limited
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- key scientific research project of higher school of Henan Province
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MMAsia '23
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