GLGFormer: Global Local Guidance Network for Mucosal Lesion Segmentation in Gastrointestinal Endoscopy Images

Tokat M, van Tilburg L, Koch AD, Spaander MC (2022) Artificial intelligence in upper gastrointestinal endoscopy. Dig Dis 40(4):395–408. https://doi.org/10.1159/000518232

Article  PubMed  Google Scholar 

Shah S, Park N, Chehade NEH, Chahine A, Monachese M, Tiritilli A, Samarasena J (2023) Effect of computer-aided colonoscopy on adenoma miss rates and polyp detection: a systematic review and meta-analysis. J Gastroenterol Hepatol 38(2):162–176. https://doi.org/10.1111/jgh.16059

Article  PubMed  Google Scholar 

Liang F, Wang S, Zhang K, Liu TJ, Li JN (2022) Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World Journal of Gastrointestinal Oncology 14(1):124. https://doi.org/10.4251/wjgo.v14.i1.124

Article  PubMed  PubMed Central  Google Scholar 

Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). Unet++: a nested u-net architecture for medical image segmentation. In 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 (pp. 3–11). Springer International Publishing. https://doi.org/10.1007/978-3-030-00889-5_1

Jha D, Smedsrud PH, Johansen D, de Lange T, Johansen HD, Halvorsen P, Riegler MA (2021) A comprehensive study on colorectal polyp segmentation with ResUNet++, conditional random field and test-time augmentation. IEEE J Biomed Health Inform 25(6):2029–2040. https://doi.org/10.1109/JBHI.2021.3049304

Article  PubMed  Google Scholar 

Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., De Lange, T., Halvorsen, P., & Johansen, H. D. (2019, December). Resunet++: an advanced architecture for medical image segmentation. In 2019 IEEE international symposium on multimedia (ISM) (pp. 225–2255). IEEE. https://doi.org/10.1109/ISM46123.2019.00049

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th International conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18 (pp. 234–241). Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28

Fan, D. P., Ji, G. P., Zhou, T., Chen, G., Fu, H., Shen, J., & Shao, L. (2020, September). Pranet: parallel reverse attention network for polyp segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 263–273). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-59725-2_26

Wu, Z., Su, L., & Huang, Q. (2019). Cascaded partial decoder for fast and accurate salient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3907–3916) https://doi.org/10.1109/CVPR.2019.00403

Kim, T., Lee, H., & Kim, D. (2021, October). Uacanet: uncertainty augmented context attention for polyp segmentation. In Proceedings of the 29th ACM international conference on multimedia (pp. 2167–2175). https://doi.org/10.1145/3474085.3475375

Lou A, Guan S, Loew M (2023) Caranet: context axial reverse attention network for segmentation of small medical objects. Journal of Medical Imaging 10(1):014005–014005. https://doi.org/10.1117/1.JMI.10.1.014005

Article  PubMed  PubMed Central  Google Scholar 

Duc NT, Oanh NT, Thuy NT, Triet TM, Dinh VS (2022) Colonformer: an efficient transformer based method for colon polyp segmentation. IEEE Access 10:80575–80586. https://doi.org/10.1109/ACCESS.2022.3195241

Article  Google Scholar 

Wu C, Long C, Li S, Yang J, Jiang F, Zhou R (2022) MSRAformer: multiscale spatial reverse attention network for polyp segmentation. Comput Biol Med 151. https://doi.org/10.1016/j.compbiomed.2022.106274

Article  CAS  PubMed  Google Scholar 

Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) SegFormer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Proces Syst 34:12077–12090

Google Scholar 

Zhang, Y., Liu, H., & Hu, Q. (2021). Transfuse: fusing transformers and cnns for medical image segmentation. In Medical image computing and computer assisted intervention–MICCAI 2021: 24th International conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24 (pp. 14–24). Springer International Publishing. https://doi.org/10.1007/978-3-030-87193-2_2

Sanderson, E., & Matuszewski, B. J. (2022, July). FCN-transformer feature fusion for polyp segmentation. In Annual conference on medical image understanding and analysis (pp. 892–907). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-12053-4_65

Dong, B., Wang, W., Fan, D. P., Li, J., Fu, H., & Shao, L. (2021). Polyp-pvt: polyp segmentation with pyramid vision transformers. arXiv preprint arXiv:2108.06932. https://doi.org/10.48550/arXiv.2108.06932

Wang, J., Huang, Q., Tang, F., Meng, J., Su, J., & Song, S. (2022, September). Stepwise feature fusion: local guides global. In International conference on medical image computing and computer-assisted intervention (pp. 110–120). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-16437-8_11

Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., & Zhang, L. (2021). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6881–6890). https://doi.org/10.1109/CVPR46437.2021.00681

Srivastava, A., Chanda, S., Jha, D., Pal, U., & Ali, S. (2022, August). GMSRF-Net: an improved generalizability with global multi-scale residual fusion network for polyp segmentation. In 2022 26th International Conference on Pattern Recognition (ICPR) (pp. 4321–4327). IEEE. https://doi.org/10.1109/ICPR56361.2022.9956726

Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., & Zhou, Y. (2021). Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306. https://doi.org/10.48550/arXiv.2102.04306

Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2022, October). Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision (pp. 205–218). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-25066-8_9

Sang, D. V., Chung, T. Q., Lan, P. N., Hang, D. V., Van Long, D., & Thuy, N. T. (2021). Ag-curesnest: a novel method for colon polyp segmentation. arXiv preprint arXiv:2105.00402. https://doi.org/10.48550/arXiv.2105.00402

Cai, L., Wu, M., Chen, L., Bai, W., Yang, M., Lyu, S., & Zhao, Q. (2022, September). Using guided self-attention with local information for polyp segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 629–638). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-16440-8_60

Zhang H, Yang X, Li D, Cui Y, Zhao J, Qiu S (2023) Dual parallel net: a novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior. J Biomed Inform 139. https://doi.org/10.1016/j.jbi.2023.104304

Article  PubMed  Google Scholar 

Wang W, Xie E, Li X, Fan DP, Song K, Liang D, Shao L (2022) Pvt v2: improved baselines with pyramid vision transformer. Computational Visual Media 8(3):415–424. https://doi.org/10.1007/s41095-022-0274-8

Article  CAS  Google Scholar 

Wei, J., Wang, S., & Huang, Q. (2020, April). F3Net: fusion, feedback and focus for salient object detection. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 07, pp. 12321–12328). https://doi.org/10.1609/aaai.v34i07.6916

Jha, D., Smedsrud, P. H., Riegler, M. A., Halvorsen, P., de Lange, T., Johansen, D., & Johansen, H. D. (2020). Kvasir-seg: a segmented polyp dataset. In MultiMedia modeling: 26th international conference, MMM 2020, Daejeon, South Korea, January 5–8, 2020, Proceedings, Part II 26 (pp. 451–462). Springer International Publishing. https://doi.org/10.1007/978-3-030-37734-2_37

Bernal J, Sánchez FJ, Fernández-Esparrach G, Gil D, Rodríguez C, Vilariño F (2015) WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99–111. https://doi.org/10.1016/j.compmedimag.2015.02.007

Article  PubMed  Google Scholar 

Tajbakhsh N, Gurudu SR, Liang J (2015) Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Med Imaging 35(2):630–644. https://doi.org/10.1109/TMI.2015.2487997

Article  PubMed  Google Scholar 

Silva J, Histace A, Romain O, Dray X, Granado B (2014) Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg 9:283–293. https://doi.org/10.1007/s11548-013-0926-3

Article  PubMed  Google Scholar 

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