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Semantic Segmentation plays a pivotal role in many applications related to medical image and video analysis.
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.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234–241. Springer, Cham (2015)
2015
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Bodenstedt, S., Speidel, S., Allan, M., Stoyanov, D., Maier-Hein, L., Kenngott, H., Wagner, M.: Multi-Instrument EndoVis Challenge Dataset. https://endovissub-instrument.grand-challenge.org/ (2015)
2015
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Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
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Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40
2018
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Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
2018
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Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
2018
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Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 418–434 (2018)
2018
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Chen, X., Zhang, R., Yan, P.: Feature fusion encoder decoder network for automatic liver lesion segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 430–433 (2019)
2019
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Ni, Z.-L., Bian, G.-B., Zhou, X.-H., Hou, Z.-G., Xie, X.-L., Wang, C., Zhou, Y.-J., Li, R.-Q., Li, Z.: Raunet: Residual attention u-net for semantic segmentation of cataract surgical instruments. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) Neural Information Processing, pp. 139–149. Springer, Cham (2019)
2019
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Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., Liu, J.: Ce-net: Context encoder network for 2d medical image segmentation. IEEE Transactions on Medical Imaging 38
2019
Cited alongside, same era.
Roy, A.G., Navab, N., Wachinger, C.: Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation” blocks. IEEE Transactions on Medical Imaging 38
2019
Cited alongside, same era.
Bogunović, H., Venhuizen, F., Klimscha, S., Apostolopoulos, S., Bab-Hadiashar, A., Bagci, U., Beg, M.F., Bekalo, L., Chen, Q., Ciller, C., Gopinath, K., Gostar, A.K., Jeon, K., Ji, Z., Kang, S.H., Koozekanani, D.D., Lu, D., Morley, D., Parhi, K.K., Park, H.S., Rashno, A., Sarunic, M., Shaikh, S., Sivaswamy, J., Tennakoon, R., Yadav, S., De Zanet, S., Waldstein, S.M., Gerendas, B.S., Klaver, C., Sánchez, C.I., Schmidt-Erfurth, U.: Retouch: The retinal oct fluid detection and segmentation benchmark and challenge. IEEE Transactions on Medical Imaging 38
2019
Cited alongside, same era.
Ghamsarian, N., Taschwer, M., Putzgruber-Adamitsch, D., Sarny, S., Schoeffmann, K.: Relevance detection in cataract surgery videos by spatio- temporal action localization. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 10720–10727 (2021)
2021
Later among the works it cites.
Ghamsarian, N., Taschwer, M., Putzgruber-Adamitsch, D., Sarny, S., El-Shabrawi, Y., Schoeffmann, K.: Lensid: a cnn-rnn-based framework towards lens irregularity detection in cataract surgery videos. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VIII 24, pp. 76–86 (2021). Springer
2021
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Ni, Z.-L., Bian, G.-B., Wang, G.-A., Zhou, X.-H., Hou, Z.-G., Xie, X.-L., Li, Z., Wang, Y.-H.: Barnet: bilinear attention network with adaptive receptive fields for surgical instrument segmentation. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 832–838 (2021)
2021
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Ghamsarian, N.: Enabling relevance-based exploration of cataract videos. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp. 378–382 (2020)
2020
Cited alongside, same era.
Ghamsarian, N., Amirpourazarian, H., Timmerer, C., Taschwer, M., Schöffmann, K.: Relevance-based compression of cataract surgery videos using convolutional neural networks. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3577–3585 (2020)
2020
Cited alongside, same era.
Ni, Z.-L., Bian, G.-B., Wang, G.-A., Zhou, X.-H., Hou, Z.-G., Chen, H.-B., Xie, X.-L.: Pyramid attention aggregation network for semantic segmentation of surgical instruments. Proceedings of the AAAI Conference on Artificial Intelligence 34
2020
Cited alongside, same era.
Feng, S., Zhao, H., Shi, F., Cheng, X., Wang, M., Ma, Y., Xiang, D., Zhu, W., Chen, X.: Cpfnet: Context pyramid fusion network for medical image segmentation. IEEE Transactions on Medical Imaging 39
2020
Cited alongside, same era.
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging 39
2020
Cited alongside, same era.
Liu, Q., Dou, Q., Yu, L., Heng, P.A.: Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data. IEEE Transactions on Medical Imaging (2020)
2020
Cited alongside, same era.
Ghamsarian, N., Taschwer, M., Putzgruber-Adamitsch, D., Sarny, S., El-Shabrawi, Y., Schöffmann, K.: Recal-net: Joint region-channel-wise calibrated network for semantic segmentation in cataract surgery videos. In: Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part III 28, pp. 391–402 (2021). Springer
2021
Later among the works it cites.
Grammatikopoulou, M., Flouty, E., Kadkhodamohammadi, A., Quellec, G., Chow, A., Nehme, J., Luengo, I., Stoyanov, D.: Cadis: Cataract dataset for surgical rgb-image segmentation. Medical Image Analysis 71
2021
Later among the works it cites.
Huang, X., Wang, H., She, C., Feng, J., Liu, X., Hu, X., Chen, L., Tao, Y.: Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy. Frontiers in Endocrinology 13
2022
Later among the works it cites.
Ghamsarian, N., Taschwer, M., Sznitman, R., Schoeffmann, K.: Deeppyramid: Enabling pyramid view and deformable pyramid reception for semantic segmentation in cataract surgery videos. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part V, pp. 276–286 (2022). Springer
2022
Later among the works it cites.
Leibetseder, A., Schoeffmann, K., Keckstein, J., Keckstein, S.: Endometriosis detection and localization in laparoscopic gynecology. Multim. Tools Appl. 81
2022
Later among the works it cites.
Ghamsarian, N., Gamazo Tejero, J., Márquez-Neila, P., Wolf, S., Zinkernagel, M., Schoeffmann, K., Sznitman, R.: Domain adaptation for medical image segmentation using transformation-invariant self-training. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 331–341 (2023). Springer
2023
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