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Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry.
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M. Z. Alom, C. Yakopcic, T. M. Taha, and V. K. Asari, “Nuclei segmentation with recurrent residual convolutional neural networks based u-net (r2u-net),” in NAECON 2018-IEEE National Aerospace and Electronics Conference . IEEE, 2018, pp. 228–233
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O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz et al. , “Attention u-net: Learning where to look for the pancreas,” Medical Image Analysis , vol. 53, no. 2, 2019, doi.org/10.1016/j.media.2019.01.012
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M. Islam, V. Vibashan, V. J. M. Jose, N. Wijethilake, U. Utkarsh, and H. Ren, “Brain tumor segmentation and survival prediction using 3d attention unet,” in International MICCAI Brainlesion Workshop . Springer, 2019, pp. 262–272
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2019
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J. C. Caicedo, A. Goodman, K. W. Karhohs, B. A. Cimini, J. Ackerman, M. Haghighi, C. Heng, T. Becker, M. Doan, C. McQuin et al. , “Nucleus segmentation across imaging experiments: the 2018 data science bowl,” Nature Mcethods , vol. 16, no. 12, pp. 1247–1253, 2019
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D. Jha, P. H. Smedsrud, M. A. Riegler, D. Johansen, T. De Lange, P. Halvorsen, and H. D. Johansen, “Resunet++: An advanced architecture for medical image segmentation,” in 2019 IEEE International Symposium on Multimedia (ISM) . IEEE, 2019, pp. 225–2255
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X. Yang, Q. Wei, C. Zhang, K. Zhou, L. Kong, and W. Jiang, “Colon polyp detection and segmentation based on improved mrcnn,” IEEE Transactions on Instrumentation and Measurement , vol. 70, pp. 1–10, 2020
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J. Fu, J. Liu, Y. Li, Y. Bao, W. Yan, Z. Fang, and H. Lu, “Contextual deconvolution network for semantic segmentation,” Pattern Recognition , vol. 101, p. 107152, 2020
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D. Jha, M. A. Riegler, D. Johansen, P. Halvorsen, and H. D. Johansen, “Doubleu-net: A deep convolutional neural network for medical image segmentation,” in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) . IEEE, 2020, pp. 558–564
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J. M. J. Valanarasu, V. A. Sindagi, I. Hacihaliloglu, and V. M. Patel, “Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 363–373
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2021
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2021
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E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
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K. Roy, D. Banik, D. Bhattacharjee, O. Krejcar, and C. Kollmann, “Lwmla-net: A lightweight multi-level attention-based network for segmentation of covid-19 lungs abnormalities from ct images,” IEEE Transactions on Instrumentation and Measurement , vol. 71, pp. 1–13, 2022
2022
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