Fourier Channel Attention Powered Lightweight Network for Image Segmentation.

Zou, F; Liu, Y; Chen, Z; Zhanghao, K; Jin, D

Fourier Channel Attention Powered Lightweight Network for Image Segmentation.

Zou, F Liu, Y Chen, Z Zhanghao, K Jin, D

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE J Transl Eng Health Med, 2023, 11, pp. 252-260
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Zou, F
dc.contributor.author	Liu, Y
dc.contributor.author	Chen, Z
dc.contributor.author	Zhanghao, K
dc.contributor.author	Jin, D https://orcid.org/0000-0003-1046-2666
dc.date.accessioned	2024-02-09T02:50:01Z
dc.date.available	2023-03-14
dc.date.available	2024-02-09T02:50:01Z
dc.date.issued	2023
dc.identifier.citation	IEEE J Transl Eng Health Med, 2023, 11, pp. 252-260
dc.identifier.issn	2168-2372
dc.identifier.issn	2168-2372
dc.identifier.uri	http://hdl.handle.net/10453/175541
dc.description.abstract	The accuracy of image segmentation is critical for quantitative analysis. We report a lightweight network FRUNet based on the U-Net, which combines the advantages of Fourier channel attention (FCA Block) and Residual unit to improve the accuracy. FCA Block automatically assigns the weight of the learned frequency information to the spatial domain, paying more attention to the precise high-frequency information of diverse biomedical images. While FCA is widely used in image super-resolution with residual network backbones, its role in semantic segmentation is less explored. Here we study the combination of FCA and U-Net, the skip connection of which can fuse the encoder information with the decoder. Extensive experimental results of FRUNet on three public datasets show that the method outperforms other advanced medical image segmentation methods in terms of using fewer network parameters and improved accuracy. It excels in pathological Section segmentation of nuclei and glands.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE J Transl Eng Health Med
dc.relation.isbasedon	10.1109/JTEHM.2023.3262841
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4203 Health services and systems
dc.subject.mesh	Cell Nucleus
dc.subject.mesh	Learning
dc.subject.mesh	Salaries and Fringe Benefits
dc.subject.mesh	Semantics
dc.subject.mesh	Spine
dc.subject.mesh	Spine
dc.subject.mesh	Cell Nucleus
dc.subject.mesh	Learning
dc.subject.mesh	Semantics
dc.subject.mesh	Salaries and Fringe Benefits
dc.subject.mesh	Cell Nucleus
dc.subject.mesh	Learning
dc.subject.mesh	Salaries and Fringe Benefits
dc.subject.mesh	Semantics
dc.subject.mesh	Spine
dc.title	Fourier Channel Attention Powered Lightweight Network for Image Segmentation.
dc.type	Journal Article
utslib.citation.volume	11
utslib.location.activity	United States
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	University of Technology Sydney/Strength - IBMD - Initiative for Biomedical Devices
utslib.copyright.status	recently_added	*
dc.date.updated	2024-02-09T02:49:59Z
pubs.publication-status	Published online
pubs.volume	11

Abstract:

The accuracy of image segmentation is critical for quantitative analysis. We report a lightweight network FRUNet based on the U-Net, which combines the advantages of Fourier channel attention (FCA Block) and Residual unit to improve the accuracy. FCA Block automatically assigns the weight of the learned frequency information to the spatial domain, paying more attention to the precise high-frequency information of diverse biomedical images. While FCA is widely used in image super-resolution with residual network backbones, its role in semantic segmentation is less explored. Here we study the combination of FCA and U-Net, the skip connection of which can fuse the encoder information with the decoder. Extensive experimental results of FRUNet on three public datasets show that the method outperforms other advanced medical image segmentation methods in terms of using fewer network parameters and improved accuracy. It excels in pathological Section segmentation of nuclei and glands.

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http://hdl.handle.net/10453/175541