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 3
IEEE/CAA Journal of Automatica Sinica
| Citation: | Z. Liu, Z. Zhang, Z. Lei, M. Omura, R.-L. Wang, and S. Gao, “Dendritic deep learning for medical segmentation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 803–805, Mar. 2024. doi: 10.1109/JAS.2023.123813
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S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523–3542, 2022.
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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, 2015, pp. 3431–3440.
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O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. Medical Image Computing and Computer-Assisted Intervention, 2015, pp. 234–241.
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V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481–2495, 2017. doi: 10.1109/TPAMI.2016.2644615
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N. Abraham and N. M. Khan, “A novel focal tversky loss function with improved attention U-Net for lesion segmentation,” in Proc. IEEE 16th Int. Symp. Biomedical Imaging, 2019, pp. 683–687.
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G. Chen, Y. Dai, and J. Zhang, “RRCNet: Refinement residual convolutional network for breast ultrasound images segmentation,” Engineering Applications of Artificial Intelligence, vol. 117, p. 105601, 2023. doi: 10.1016/j.engappai.2022.105601
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M. E. Larkum, “Are dendrites conceptually useful?” Neuroscience, vol. 489, pp. 4–14, 2022.
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S. Gao, M. Zhou, Y. Wang, J. Cheng, H. Yachi, and J. Wang, “Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 2, pp. 601–614, 2019. doi: 10.1109/TNNLS.2018.2846646
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S. Gao, M. Zhou, Z. Wang, D. Sugiyama, J. Cheng, J. Wang, and Y. Todo, “Fully complex-valued dendritic neuron model,” IEEE Trans. Neural Networks and Learning Systems, vol. 34, no. 4, pp. 2105–2118, 2023. doi: 10.1109/TNNLS.2021.3105901
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X. Ning, W. Tian, Z. Yu, W. Li, X. Bai, and Y. Wang, “HCFNN: Highorder coverage function neural network for image classification,” Pattern Recognition, vol. 131, p. 108873, 2022. doi: 10.1016/j.patcog.2022.108873
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S. Jin, S. Yu, J. Peng, H. Wang, and Y. Zhao, “A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning,” Scientific Reports, vol. 13, no. 1, p. 6762, 2023. doi: 10.1038/s41598-023-33357-y
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