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Colonoscopy is a gold standard procedure but is highly operator-dependent.
Rex, D.K., Cutler, C.S., Lemmel, G.T., Rahmani, E.Y., Clark, D.W., Helper, D.J., Lehman, G.A., Mark, D.G.: Colonoscopic miss rates of adenomas determined by back-to-back colonoscopies. Gastroenterology 112
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Kim, N.H., Jung, Y.S., Jeong, W.S., Yang, H.J., Park, S.K., Choi, K., Park, D.I.: Miss rate of colorectal neoplastic polyps and risk factors for missed polyps in consecutive colonoscopies. Intestinal research 15
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Heinzerling, B., Strube, M.: BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) (2018)
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Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp. 3–19 (2018)
2018
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Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: Parallel reverse attention network for polyp segmentation. In: Proceedings of the International conference on medical image computing and computer-assisted intervention (MICCAI). pp. 263–273 (2020)
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Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., Lange, T.d., Johansen, D., Johansen, H.D.: Kvasir-SEG: a segmented polyp dataset. In: Proceedings of the International Conference on Multimedia Modeling (MMM). pp. 451–462 (2020)
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Jha, D., Ali, S., Tomar, N.K., Johansen, H.D., Johansen, D., Rittscher, J., Riegler, M.A., Halvorsen, P.: Real-time polyp detection, localization and segmentation in colonoscopy using deep learning. IEEE Access 9
2021
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Jha, D., Smedsrud, P.H., Johansen, D., de Lange, T., Johansen, H.D., Halvorsen, P., Riegler, M.A.: A comprehensive study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation. IEEE journal of Biomedical and Health Informatics 25
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Shen, Y., Jia, X., Meng, M.Q.H.: Hrenet: A hard region enhancement network for polyp segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). pp. 559–568 (2021)
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Cited alongside, same era.
Zhong, J., Wang, W., Wu, H., Wen, Z., Qin, J.: Polypseg: An efficient context-aware network for polyp segmentation from colonoscopy videos. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). pp. 285–294 (2020)
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2021
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2021
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Srivastava, A., Jha, D., Chanda, S., Pal, U., Johansen, H.D., Johansen, D., Riegler, M.A., Ali, S., Halvorsen, P.: MSRF-Net: A Multi-scale Residual Fusion Network for Biomedical Image Segmentation. IEEE Journal of biomedical imaging and health informatics (2021)
2021
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Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 71
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