Vision Mamba UNet:Vision Mamba UNet for Polyp Segmentation
Abstract
polyps in the early stage. To accurately remove the polyp, we need to identify the size and boundary of the polyp. In this paper, VM-UNet is
used to segment polyp images. This network overcomes the inherent shortcomings of convolutional neural network (CNN) and transformer,
and not only performs well in remote interaction modeling, but also maintains linear computational complexity. Our model was applied to the
polyp segmentation Kvasir-SEG, CVC-ClinicDB, CVC-300, CVC-ColonDB, and ETIS datasets. Showing strong performance.
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[1] Chi Z, Lin Y, Huang J, et al. Risk factors for recurrence of colorectal conventional adenoma and serrated polyp[J]. Gastroenterology report, 2022, 10: goab038.
[2] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//Medical image computing
and computer-assisted interventionMICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings,
part III 18. Springer International Publishing, 2015: 234-241.
[3] Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. Unet++: A nested u-net architecture for medical image segmentation[C]//Deep
Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA
2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018,
Proceedings 4. Springer International Publishing, 2018: 3-11.
[4] Gu A, Dao T. Mamba: Linear-time sequence modeling with selective state spaces[J]. arxiv preprint arxiv:2312.00752, 2023.
[5] Ruan J, **ang S. Vm-unet: Vision mamba unet for medical image segmentation[J]. arxiv preprint arxiv:2402.02491, 2024.
[6] 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 58, 2020, proceedings, part II 26. Springer International Publishing, 2020: 451-462.
[7] Bernal J, Snchez F J, Fernndez-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.
[8] 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.
[9] Vzquez D, Bernal J, Snchez F J, et al. A benchmark for endoluminal scene segmentation of colonoscopy images[J]. Journal of healthcare engineering, 2017, 2017(1): 4037190.
[10] 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: http://dx.doi.org/10.70711/mhr.v2i3.4892
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