CSA-Net: A Transformer-based Polyp Segmentation Network
DOI:
https://doi.org/10.54097/y0dv2v19Keywords:
Deep learning, Polyp segmentation, transformer.Abstract
It is well known that colorectal polyps are a precursor to colorectal cancer. Accurate segmentation of polyp images from colonoscopy can assist clinicians in localizing polyp regions and reduce the occurrence of misdiagnosis accurately. Many existing methods achieve good results in the polyp segmentation task, but their extraction of global and local features is often insufficient. In this paper, we propose a transformer-based polyp segmentation network (CSA-Net) that utilizes two types of attention modules- spatial attention and channel attention-to further adaptively fuse local features with their global features. The proposed network is validated on five polyp datasets. Experimental results show that our model outperforms previously proposed models.
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Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer International Publishing, 2015: 234-241. DOI: https://doi.org/10.1007/978-3-319-24574-4_28
Cao H, Wang Y, Chen J, et al. Swin-unet: Unet-like pure transformer for medical image segmentation[C]//European conference on computer vision. Cham: Springer Nature Switzerland, 2022: 205-218. DOI: https://doi.org/10.1007/978-3-031-25066-8_9
Chen S, Tan X, Wang B, et al. Reverse attention for salient object detection[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 234-250. DOI: https://doi.org/10.1007/978-3-030-01240-3_15
Fan D P, Ji G P, Zhou T, et al. Pranet: Parallel reverse attention network for polyp segmentation[C]//International conference on medical image computing and computer-assisted intervention. Cham: Springer International Publishing, 2020: 263-273. DOI: https://doi.org/10.1007/978-3-030-59725-2_26
Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020.
Wang J, Huang Q, Tang F, et al. Stepwise feature fusion: Local guides global[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2022: 110-120. DOI: https://doi.org/10.1007/978-3-031-16437-8_11
Islam M A, Jia S, Bruce N D B. How much position information do convolutional neural networks encode?[J]. arXiv preprint arXiv:2001.08248, 2020.
Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2881-2890. DOI: https://doi.org/10.1109/CVPR.2017.660
Peng C, Zhang X, Yu G, et al. Large kernel matters--improve semantic segmentation by global convolutional network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4353-4361. DOI: https://doi.org/10.1109/CVPR.2017.189
Fu J, Liu J, Tian H, et al. Dual attention network for scene segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 3146-3154. DOI: https://doi.org/10.1109/CVPR.2019.00326
Mou L, Zhao Y, Chen L, et al. CS-Net: Channel and spatial attention network for curvilinear structure segmentation[C]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I 22. Springer International Publishing, 2019: 721-730. DOI: https://doi.org/10.1007/978-3-030-32239-7_80
Silva J, Histace A, Romain O, et al. Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer[J]. International journal of computer assisted radiology and surgery, 2014, 9: 283-293. DOI: https://doi.org/10.1007/s11548-013-0926-3
Bernal J, Sánchez F J, Fernández-Esparrach G, et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians[J]. Computerized medical imaging and graphics, 2015, 43: 99-111. DOI: https://doi.org/10.1016/j.compmedimag.2015.02.007
Tajbakhsh N, Gurudu S R, Liang J. Automated polyp detection in colonoscopy videos using shape and context information[J]. IEEE transactions on medical imaging, 2015, 35(2): 630-644. DOI: https://doi.org/10.1109/TMI.2015.2487997
Vázquez D, Bernal J, Sánchez F J, et al. A benchmark for endoluminal scene segmentation of colonoscopy images[J]. Journal of healthcare engineering, 2017, 2017. DOI: https://doi.org/10.1155/2017/4037190
Jha D, Smedsrud P H, Riegler M A, et al. Kvasir-seg: A segmented polyp dataset[C]//MultiMedia Modeling: 26th International Conference, MMM 2020, Daejeon, South Korea, January 5–8, 2020, Proceedings, Part II 26. Springer International Publishing, 2020: 451-462. DOI: https://doi.org/10.1007/978-3-030-37734-2_37
Loshchilov I, Hutter F. Decoupled Weight Decay Regularization[C]//International Conference on Learning Representations. 2018.
Chen J, Lu Y, Yu Q, et al. Transunet: Transformers make strong encoders for medical image segmentation[J]. arXiv preprint arXiv:2102.04306, 2021.
Wei J, Hu Y, Zhang R, et al. Shallow attention network for polyp segmentation[C]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24. Springer International Publishing, 2021: 699-708. DOI: https://doi.org/10.1007/978-3-030-87193-2_66
Fang Y, Chen C, Yuan Y, et al. Selective feature aggregation network with area-boundary constraints for polyp segmentation[C]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I 22. Springer International Publishing, 2019: 302-310. DOI: https://doi.org/10.1007/978-3-030-32239-7_34
Patel K, Bur A M, Wang G. Enhanced u-net: A feature enhancement network for polyp segmentation[C]//2021 18th Conference on Robots and Vision (CRV). IEEE, 2021: 181-188. DOI: https://doi.org/10.1109/CRV52889.2021.00032
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