A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 11
Issue 5
IEEE/CAA Journal of Automatica Sinica
| Citation: | P. Hu, X. Deng, F. Tan, and L. Hu, “Multi-axis attention with convolution parallel block for organoid segmentation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 5, pp. 1295–1297, May 2024. doi: 10.1109/JAS.2023.124026
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K. Kretzschmar and H. Clevers, “Organoids: Modeling development and the stem cell niche in a dish,” Dev. CELL, vol. 38, no. 6, pp. 590–600, Sept. 2016. doi: 10.1016/j.devcel.2016.08.014
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A. Fatehullah, S. H. Tan, and N. Barker, “Organoids as an in vitro model of human development and disease,” Nat. CELL Biol., vol. 18, no. 3, pp. 246–254, Mar. 2016. doi: 10.1038/ncb3312
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T. Wang, X. Xu, F. M. Shen, and Y. Yang", “A Cognitive memory-augmented network for visual anomaly detection,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1296–1307, Jul. 2021. doi: 10.1109/JAS.2021.1004045
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C. Lee, H. Hasegawa, and S. Gao, “Complex-valued neural networks: A comprehensive survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1406–1426, Aug. 2022. doi: 10.1109/JAS.2022.105743
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P. Huang, J. Han, N. Liu, J. Ren, and D. Zhang, “Scribble-supervised video object segmentation,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 339–353, Feb. 2022. doi: 10.1109/JAS.2021.1004210
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S. Pare, A. Kumar, V. Bajaj, and G. K. Singh, “A context sensitive multilevel thresholding using swarm based algorithms,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1471–1486, Nov. 2019. doi: 10.1109/JAS.2017.7510697
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Y. Xia, H. Yu, and F.-Y. Wang, “Accurate and robust eye center localization via fully convolutional networks,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1127–1138, Sept. 2019. doi: 10.1109/JAS.2019.1911684
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Z. Tu, H. Talebi, H. Zhang, et al., “MaxViT: Multi-axis vision transformer,” in Computer Vision – ECCV 2022, S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, and T. Hassner, Eds., Cham, Switzerland: Springer Nature Switzerland, 2022, pp. 459–479.
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. Advances in Neural Information Processing Systems 30: Annu. Conf. Neural Information Processing Systems, Long Beach, USA, 2017, pp. 6000–6010.
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J. M. Matthews, B. Schuster, S. S. Kashaf, et al., “OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics,” PLOS Comput. Biol., vol. 18, no. 11, p. e1010584, 2022. doi: 10.1371/journal.pcbi.1010584
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A. K. Bhandari, A. Ghosh, and I. V. Kumar, “A local contrast fusion based 3D OTSU algorithm for multilevel image segmentation,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 200–213, Jan. 2020. doi: 10.1109/JAS.2019.1911843
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V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 12, pp. 2481–2495, 2017. doi: 10.1109/TPAMI.2016.2644615
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O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, and Rueckert. “Attention U-NET: Learning where to look for the pancreas,” arXiv preprint arXiv: 1804.03999, 2018.
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Boston, USA, 2015, pp. 3431–3440.
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J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, “Dual attention network for scene segmentation,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2019, pp. 3146–3154.
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A. G. Roy, N. Navab, and C. Wachinger, “Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks,” in Proc. Medical Image Computing and Computer Assisted Intervention, 2018.
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