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 6
IEEE/CAA Journal of Automatica Sinica
| Citation: | P. Wu, H. Li, L. Hu, J. Ge, and N. Zeng, “A local-global attention fusion framework with tensor decomposition for medical diagnosis,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1536–1538, Jun. 2024. doi: 10.1109/JAS.2023.124167
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L. Sun, Z. Liu, X. Sun, L. Liu, R. Lan, and X. Luo, “Lightweight image super-resolution via weighted multi-scale residual network,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1271–1280, 2021. doi: 10.1109/JAS.2021.1004009
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H. Li, N. Zeng, P. Wu, and K. Clawson, “Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision,” Expert Systems Applications, vol. 207, p. 118029, 2022.
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H. Li, P. Wu, Z. Wang, J. Mao, F.-E. Alsaadi, and N. Zeng, “A generalized framework of feature learning enhanced convolutional neural network for pathology-image-oriented cancer diagnosis,” Computers in Biology and Medicine, vol. 151, p. 106265, 2023.
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P. Wu, Z. Wang, B. Zheng, H. Li, F.-E. Alsaadi, and N. Zeng, “AGGN: Attention-based glioma grading network with multi-scale feature extraction and multi-modal information fusion,” Computers in Biology and Medicine, vol. 152, p. 106457, 2023.
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Y. Lei, H. Zhu, J. Zhang, and H. Shan, “Meta ordinal regression forest for medical image classification with ordinal labels,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1233–1247, 2022. doi: 10.1109/JAS.2022.105668
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D. Wu and X. Luo, “Robust latent factor analysis for precise representation of high-dimensional and sparse data,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 796–805, 2021. doi: 10.1109/JAS.2020.1003533
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X. Luo, Y. Yuan, S. Chen, N. Zeng, and Z. Wang, “Position-transitional particle swarm optimization-incorporated latent factor analysis,” IEEE Trans. Knowledge and Data Engineering, vol. 34, no. 8, pp. 3958–3970, 2022. doi: 10.1109/TKDE.2020.3033324
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N. Zeng, P. Wu, Z. Wang, H. Li, W. Liu, and X. Liu, “A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection,” IEEE Trans. Instrumentation and Measurement, vol. 71, p. 3507014, 2022.
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L. Chen and X. Luo, “Tensor distribution regression based on the 3D conventional neural networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1628–1630, 2023. doi: 10.1109/JAS.2023.123591
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Q. Cheng, Y. Zhou, H. Huang, and Z. Wang, “Multi-attention fusion and fine-grained alignment for bidirectional image-sentence retrieval in remote sensing,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1532–1535, 2022. doi: 10.1109/JAS.2022.105773
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M. Guo, C. Lu, Z. Liu, M. Cheng, and S. Hu, “CSWin transformer: A general vision transformer backbone with cross-shaped windows,” in Proc. 35th IEEE/CVF Computer Vision and Pattern Recognition Conf., 2022, pp. 1–11.
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Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “ECA-Net: Efficient channel attention for deep convolutional neural networks,” in Proc. 33rd IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2020, pp. 11531–11539.
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X. Li, Z. Zhong, J. Wu, Y. Yang, Z. Lin, and H. Liu, “Expectation-maximization attention networks for semantic segmentation”, in Proc. 23rd Int. Conf. Computer Vision, 2019.
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G. Xu, X. Wang, and X. Xu, “Single image enhancement in sandstorm weather via tensor least square,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1649–1661, 2020. doi: 10.1109/JAS.2020.1003423
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F. Bi, X. Luo, B. Shen, H. Dong, and Z. Wang, “Proximal alternating-direction-method-of-multipliers-incorporated nonnegative latent factor analysis,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1388–1406, 2023. doi: 10.1109/JAS.2023.123474
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Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A ConvNet for the 2020s,” in Proc. 35th IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2022, pp. 11966–11976.
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L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z. Jiang, F. Tay, J. Feng, and S. Yan, “Tokens-to-Token ViT: Training vision transformers from scratch on ImageNet,” in Proc. 25th IEEE/CVF Int. Conf. Computer Vision, 2021, pp. 538–547.
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K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: 1409.1556, 2015.
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Z. Peng, Z. Guo, W. Huang, Y. Wang, L. Xie, J. Jiao, Q. Tian, and Q. Ye, “Conformer: Local features coupling global representations for recognition and detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 9454–9468, 2023. doi: 10.1109/TPAMI.2023.3243048
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X. Huo, G. Sun, S. Tian, Y. Wang, L. Yu, J. Long, W. Zhang, and A. Li, “HiFuse: Hierarchical multi-scale feature fusion network for medical image classification,” arXiv preprint arXiv: 2209.10218, 2022.
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N. Ma, X. Zhang, H. Zheng, and J. Sun, “ShuffleNet V2: Practical guidelines for efficient CNN architecture design,” in Proc. 15th European Conf. Computer Vision, 2018, vol. 11218, pp. 122–138.
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