Abstract
We propose a robust framework called ESSL-Polyp combining ensemble and semi-supervised learning to improve polyp segmentation accuracy. The intuition starts from our previous experiments with semi-supervised learning on polyp segmentation. Following that, the semi-supervised models usually generalize better than supervised models with the same amount of training data, especially in out-of-domain datasets. In this paper, instead of using all labeled data, we split it into k-fold sub-datasets with labeled and unlabeled parts to train corresponding semi-supervised models. The ensemble of semi-supervised models is utilized to generate final precise predictions. We achieve an average of 0.8557 Dice score on five popular benchmark datasets, including Kvarsir, CVC-ClinicDB, ETIS-LaribPolypDB, CVC-ColonDB, and CVC-300. Meanwhile, the supervised baseline using the same training dataset only has an average Dice score of 0.8264. Our method especially yields superior performance compared to the supervised approach in out-of-domain datasets such as ETIS-LaribPolypDB, CVC-ColonDB, and CVC-300. The source code and pre-trained models are available at https://sal.vn/essl-polyp.
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References
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Chaitanya, K., Karani, N., Baumgartner, C.F., Erdil, E., Becker, A., Donati, O., Konukoglu, E.: Semi-supervised task-driven data augmentation for medical image segmentation. Med. Image Anal. 68, 101934 (2021)
Van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Mach. Learn. 109(2), 373–440 (2020)
Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. In: Advances in Neural Information Processing Systems, vol. 33, pp. 596–608 (2020)
Zhang, B., et al.: Flexmatch: boosting semi-supervised learning with curriculum pseudo labeling. Adv. Neural. Inf. Process. Syst. 34, 18408–18419 (2021)
Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. In: Advances in Neural Information Processing Systems, vol. 7 (1994)
Quinlan, J.R.: Bagging, boosting, and C4. 5. In: AAAI/IAAI, vol. 1, pp. 725–730 (1996)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: ICML, vol. 96 (1996)
Huang, C.-H., Wu, H.-Y., Lin, Y.-L.: Hardnet-MSEG: a simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 fps. arXiv preprint arXiv:2101.07172 (2021)
Zhang, Y., Liu, H., Hu, Q.: TransFuse: fusing transformers and CNNs for medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 14–24. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_2
Duc, N.T., Oanh, N.T., Thuy, N.T., Triet, T.M., Dinh, V.S.: Colonformer: an efficient transformer based method for colon polyp segmentation. IEEE Access. 10, 80575–80586 (2022)
Zhang, Y., Gong, Z., Zheng, X., Zhao, X., Yao, W.: Semi-supervision semantic segmentation with uncertainty-guided self cross supervision. arXiv preprint arXiv:2203.05118 (2022)
Li, Y., Data, G.W.P., Fu, Y., Hu, Y., Prisacariu, V.A.: Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes. arXiv preprint arXiv:2110.11742 (2021)
Wang, Y., et al.: Semi-Supervised semantic segmentation using unreliable pseudo-labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4248-4257 (2022)
Van, T.P., et al.: Online pseudo labeling for polyp segmentation with momentum networks. arXiv preprint arXiv:2209.14599 (2022)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1), 1–39 (2010)
Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., Keutzer, K.: Densenet: implementing efficient convnet descriptor pyramids. arXiv preprint arXiv:1404.1869 (2014)
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)
Fang, Y., Chen, C., Yuan, Y., Tong, K.: Selective feature aggregation network with area-boundary constraints for polyp segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 302–310. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_34
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Fan, D.-P., et al.: PraNet: parallel reverse attention network for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 263–273. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_26
Zhao, X., Zhang, L., Lu, H.: Automatic polyp segmentation via multi-scale subtraction network. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 120–130. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_12
Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 699–708. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_66
Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 451–462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37734-2_37
Bernal, J.F., et al.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 43, 99–111 (2015)
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imaging 35(2), 630–644 (2015)
Vázquez, D., et al.: A benchmark for endoluminal scene segmentation of colonoscopy images. J. Healthcare Eng. 2017 (2017)
Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9(2), 283–293 (2014)
Acknowledgment
The project described in this paper was funded by Vingroup Innovation Foundation (VINIF) under project code VINIF.2020.DA17. Additionally, this work received partial support from Sun-Asterisk Inc. The authors express their gratitude to their colleagues at Sun-Asterisk Inc for their valuable advice and expertise, which proved to be instrumental in the successful completion of this experiment.
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Van, T.P., Viet, S.D. (2023). ESSL-Polyp: A Robust Framework of Ensemble Semi-supervised Learning in Polyp Segmentation. In: Arai, K. (eds) Intelligent Computing. SAI 2023. Lecture Notes in Networks and Systems, vol 739. Springer, Cham. https://doi.org/10.1007/978-3-031-37963-5_4
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DOI: https://doi.org/10.1007/978-3-031-37963-5_4
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